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Lots-of-LoRAs/task1221_ted_translation_en_he | Lots-of-LoRAs | "2025-01-01T14:28:14Z" | 32 | 0 | [
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"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-01T14:28:12Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task1221_ted_translation_en_he
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- name: id
dtype: string
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---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1221_ted_translation_en_he
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
kubramt1/tes111 | kubramt1 | "2025-01-01T17:48:37Z" | 32 | 0 | [
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jalilkartal/AZB_EN_Combined_47m_tokenized | jalilkartal | "2025-01-01T20:39:02Z" | 32 | 0 | [
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license: apache-2.0
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mytestdpo/llama3_gsm8k1_w2c74.5K_c175K_c2c40K | mytestdpo | "2025-01-01T19:06:00Z" | 32 | 0 | [
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|
jlitch/so100_arm_test | jlitch | "2025-01-01T23:18:14Z" | 32 | 0 | [
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license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- tutorial
configs:
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data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
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"observation.images.external": {
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480,
640,
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"channels"
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1
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"names": null
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"names": null
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"names": null
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"task_index": {
"dtype": "int64",
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1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
jkazdan/gemma-2-9b-It-100-refusal-Hex-PHI | jkazdan | "2025-01-02T01:50:37Z" | 32 | 0 | [
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jkazdan/Meta-Llama-3-8B-Instruct-harmful-4800-hexphi | jkazdan | "2025-01-02T02:48:29Z" | 32 | 0 | [
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RyanYr/reflect_Om2G8kOm2AgG8k_problems | RyanYr | "2025-01-02T04:57:31Z" | 32 | 0 | [
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DT4LM/t5v1-1ba_rte_clare_differential_original | DT4LM | "2025-01-02T05:28:39Z" | 32 | 0 | [
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DT4LM/albertbase_mr_kuleshov_var_differential_original | DT4LM | "2025-01-02T05:57:14Z" | 32 | 0 | [
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DT4LM/debertav3ba_rte_kuleshov_var_differential | DT4LM | "2025-01-02T06:32:57Z" | 32 | 0 | [
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DT4LM/t5v1-1ba_rte_kuleshov_var_differential | DT4LM | "2025-01-03T12:01:10Z" | 32 | 0 | [
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DT4LM/gp_mr_kuleshov_var_differential_original | DT4LM | "2025-01-02T06:06:09Z" | 32 | 0 | [
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DT4LM/gp_rte_kuleshov_var_differential_original | DT4LM | "2025-01-02T06:14:29Z" | 32 | 0 | [
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XAT928/dataset_sorana_3year | XAT928 | "2025-01-02T08:57:57Z" | 32 | 0 | [
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|
linlihao331/Aloha_training_dataset | linlihao331 | "2025-01-02T07:18:09Z" | 32 | 0 | [
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pretty_name: "\tAloha HDF5 Robot Control Dataset"
--- |
haorandai/Jan1_PGD_Mice_Orange_10samples_3constraints | haorandai | "2025-01-02T08:47:17Z" | 32 | 0 | [
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haorandai/Jan1_Random_Mice_UF_10samples_3constraints | haorandai | "2025-01-02T08:51:22Z" | 32 | 0 | [
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haorandai/Jan1_Clean_Banana_UF_10samples_3constraints | haorandai | "2025-01-02T08:56:20Z" | 32 | 0 | [
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haorandai/Jan1_Clean_Mice_UF_10samples_3constraints | haorandai | "2025-01-02T08:59:41Z" | 32 | 0 | [
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celinah/openai_records_c04689c1 | celinah | "2025-01-02T10:57:12Z" | 32 | 0 | [
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tags:
- observers
- openai
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
InsultedByMathematics/llama3-ultrafeedback-armo-test-evaluation-rewards-logprobs_update_401_online | InsultedByMathematics | "2025-01-02T11:56:02Z" | 32 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-01-02T11:56:01Z" | ---
dataset_info:
features:
- name: finetuned_response_0
dtype: string
- name: finetuned_response_1
dtype: string
- name: finetuned_response_2
dtype: string
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dtype: string
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dtype: string
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- name: llama_prompt_tokens
sequence: int64
- name: chosen_reward
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- name: finetuned_llama_prompt_tokens
sequence: int64
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- name: finetuned_chosen
dtype: string
- name: finetuned_chosen_reward
dtype: float64
- name: finetuned_llama_chosen_tokens
sequence: int64
- name: base_llama_chosen_tokens
sequence: int64
- name: finetuned_reject
dtype: string
- name: finetuned_reject_reward
dtype: float64
- name: finetuned_llama_reject_tokens
sequence: int64
- name: base_llama_reject_tokens
sequence: int64
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- name: finetuned_middle_reward
dtype: float64
- name: finetuned_llama_middle_tokens
sequence: int64
- name: base_llama_middle_tokens
sequence: int64
- name: finetuned_chosen_logprob
dtype: float64
- name: finetuned_middle_logprob
dtype: float64
- name: finetuned_reject_logprob
dtype: float64
- name: base_chosen_logprob
dtype: float64
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dtype: float64
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dtype: float64
splits:
- name: test_prefs
num_bytes: 10171913
num_examples: 119
download_size: 1064058
dataset_size: 10171913
configs:
- config_name: default
data_files:
- split: test_prefs
path: data/test_prefs-*
---
|
Lots-of-LoRAs/task1230_ted_translation_ar_en | Lots-of-LoRAs | "2025-01-02T14:07:39Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-02T14:07:36Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task1230_ted_translation_ar_en
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 5136
- name: valid
num_examples: 642
- name: test
num_examples: 642
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1230_ted_translation_ar_en
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
Lots-of-LoRAs/task514_argument_consequence_classification | Lots-of-LoRAs | "2025-01-02T14:08:03Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-02T14:08:01Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task514_argument_consequence_classification
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 137
- name: valid
num_examples: 17
- name: test
num_examples: 18
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task514_argument_consequence_classification
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
Lots-of-LoRAs/task062_bigbench_repeat_copy_logic | Lots-of-LoRAs | "2025-01-02T14:08:26Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-02T14:08:23Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task062_bigbench_repeat_copy_logic
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 23
- name: valid
num_examples: 3
- name: test
num_examples: 3
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task062_bigbench_repeat_copy_logic
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
Lots-of-LoRAs/task112_asset_simple_sentence_identification | Lots-of-LoRAs | "2025-01-02T14:16:38Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-02T14:16:36Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task112_asset_simple_sentence_identification
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 5200
- name: valid
num_examples: 650
- name: test
num_examples: 650
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task112_asset_simple_sentence_identification
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
Lots-of-LoRAs/task279_stereoset_classification_stereotype | Lots-of-LoRAs | "2025-01-02T14:19:59Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-02T14:19:57Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task279_stereoset_classification_stereotype
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 5192
- name: valid
num_examples: 649
- name: test
num_examples: 649
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task279_stereoset_classification_stereotype
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
RyanYr/reflect_llama8b-t0_llama33-t12_om2-300to500k | RyanYr | "2025-01-02T14:35:00Z" | 32 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-01-02T14:34:37Z" | ---
dataset_info:
features:
- name: problem
dtype: string
- name: generated_solution
dtype: string
- name: answer
dtype: string
- name: problem_source
dtype: string
- name: response@0
sequence: string
- name: response@1
sequence: string
- name: response@2
sequence: string
splits:
- name: train
num_bytes: 1741242954
num_examples: 200000
download_size: 714375623
dataset_size: 1741242954
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Lots-of-LoRAs/task1354_sent_comp_classification | Lots-of-LoRAs | "2025-01-02T14:37:52Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-02T14:37:50Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task1354_sent_comp_classification
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 799
- name: valid
num_examples: 100
- name: test
num_examples: 100
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1354_sent_comp_classification
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
Lots-of-LoRAs/task1193_food_course_classification | Lots-of-LoRAs | "2025-01-02T14:48:49Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-02T14:48:47Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task1193_food_course_classification
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
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num_examples: 176
- name: valid
num_examples: 22
- name: test
num_examples: 22
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1193_food_course_classification
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
FiscaAI/chop-alpha | FiscaAI | "2025-01-02T18:18:43Z" | 32 | 0 | [
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] | null | "2025-01-02T18:18:41Z" | ---
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---
|
ZhangShenao/new-Mistral-7B-Instruct-v0.2-iter1_sample_1000_tp | ZhangShenao | "2025-01-02T21:43:58Z" | 32 | 0 | [
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---
|
amuvarma/amu-zucktts-with-qaudio-total | amuvarma | "2025-01-03T02:04:11Z" | 32 | 0 | [
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|
antitheft159/_600_soil_classification | antitheft159 | "2025-01-03T02:10:48Z" | 32 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2025-01-03T02:10:38Z" | ---
license: apache-2.0
---
|
Kichud02/turiyatree_data | Kichud02 | "2025-01-03T05:06:52Z" | 32 | 0 | [
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] | null | "2025-01-03T05:05:52Z" | ---
license: apache-2.0
---
|
DT4LM/t5v1-1base_mr_kuleshov_var | DT4LM | "2025-01-03T05:37:06Z" | 32 | 0 | [
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|
DT4LM/t5v1-1base_rte_pair_kuleshov_var | DT4LM | "2025-01-03T11:48:58Z" | 32 | 0 | [
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---
|
jkazdan/gemma-2-2b-it-refusal-attack | jkazdan | "2025-01-03T06:52:06Z" | 32 | 0 | [
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---
|
DT4LM/t5v1-1ba_mr_kuleshov_var_differential_original | DT4LM | "2025-01-03T07:11:36Z" | 32 | 0 | [
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---
|
dgambettavuw/D_gen1_run2_llama2-7b_xlsum_doc1000_real64_synt64_vuw | dgambettavuw | "2025-01-03T08:34:16Z" | 32 | 0 | [
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---
|
DT4LM/albertbasev2_mrpc_pair_kuleshov_var | DT4LM | "2025-01-03T09:30:33Z" | 32 | 0 | [
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path: data/train-*
---
|
dunghuynh/SalBench | dunghuynh | "2025-01-06T07:45:41Z" | 32 | 0 | [
"license:mit",
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"library:dask",
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] | null | "2025-01-03T10:00:47Z" | ---
license: mit
dataset_info:
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features:
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dtype: string
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dtype: image
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sequence: image
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sequence: string
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num_examples: 2589
configs:
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path: O3/shard*
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data_files:
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path: O3_3shots/shard*
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data_files:
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path: O3_5shots/shard*
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data_files:
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path: O3_box/shard*
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data_files:
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path: O3_box_3shots/shard*
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data_files:
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path: O3_box_5shots/shard*
- config_name: O3_box_img
data_files:
- split: test
path: O3_box_img/shard*
- config_name: O3_box_img_3shots
data_files:
- split: test
path: O3_box_img_3shots/shard*
- config_name: O3_box_img_5shots
data_files:
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path: O3_box_img_5shots/shard*
- config_name: P3
data_files:
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path: P3/shard*
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data_files:
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path: P3_3shots/shard*
- config_name: P3_5shots
data_files:
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path: P3_5shots/shard*
- config_name: P3_box
data_files:
- split: test
path: P3_box/shard*
- config_name: P3_box_3shots
data_files:
- split: test
path: P3_box_3shots/shard*
- config_name: P3_box_5shots
data_files:
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path: P3_box_5shots/shard*
- config_name: P3_box_img
data_files:
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path: P3_box_img/shard*
- config_name: P3_box_img_3shots
data_files:
- split: test
path: P3_box_img_3shots/shard*
- config_name: P3_box_img_5shots
data_files:
- split: test
path: P3_box_img_5shots/shard*
---
# *SalBench: A Benchmark for Evaluating Perceptual Capabilities of Vision-Language Models*
– *Ngoc Dung Huynh, Yasser Abdelaziz Dahou Djilali, Le Khac Phuc, Ankit Singh, Wamiq Para, Sanath Narayan*
[[💻 Github](https://github.com/dunghuynhandy/SalBench)] [[📊 Leaderboard ](https://github.com/dunghuynhandy/SalBench)][[📖 ArXiv Paper](Comming Soon)]
## Introduction
We present Saliency Benchmark (SalBench), a novel benchmark designed to assess the capability of Large Vision-Language Models (LVLM) in detecting visually salient features that are readily apparent to humans, such as a large circle amidst a grid of smaller ones. This benchmark focuses on low-level features including color, intensity, and orientation, which are fundamental to human visual processing. Our SalBench consists of images that highlight rare, unusual, or unexpected elements within scenes, and naturally draw human attention. It comprises three novel tasks for evaluating the perceptual capabilities of LVLM: Odd-One-Out Detection, Referring Odd-One-Out, and Visual Referring Odd-One-Out. We perform a comprehensive evaluation of state-of-the-art LVLM using SalBench and our findings reveal a surprising limitation: LVLM struggle to identify seemingly obvious visual anomalies, with even the advanced GPT-4o achieving only 47.6\% accuracy on such a simple task. SalBench will be an important step in measuring the capabilities of LVLM that align with the subtle definition of human attention.
### Key Tasks in SalBench
#### 1. Salient Object Detection
- **Objective**: Evaluate the model's ability to identify and segment the most visually important objects in an image.
- **Description**: The model is tasked with distinguishing salient objects from the background, mimicking human attention.
- **Significance**: Critical for applications like autonomous driving and medical imaging where detecting key objects is vital.
#### 2. Visual Question Answering (VQA) on Salient Regions
- **Objective**: Test the model's ability to answer questions that require attention to specific, salient regions of an image.
- **Description**: The model must extract relevant information from highlighted regions to provide accurate answers.
- **Significance**: Measures the integration of visual perception and language understanding.
#### 3. Referring Expression Segmentation
- **Objective**: Assess the model’s capacity to segment objects based on natural language descriptions.
- **Description**: The model must accurately segment the object referred to by a user-provided textual phrase.
- **Significance**: Important for human-computer interaction, allowing intuitive control through verbal instructions.
### Visualization
<!-- ![Description of image](){width=500 height=300} -->
<!-- <img src="./images/abstract_fig.png" alt="Example Image" width="400"> -->
<div align="center">
<img src="./images/abstract_fig.png" alt="Description of image" width="800">
</div>
## Leaderboard
#### + Exact Match and F1-Scores on the synthetic image set (**P3**) of SalBench.
<table>
<tr style="border-top: 2px solid black;">
<th rowspan="3">Model</th>
<th rowspan="3" style="text-align: center; border-right: 1px solid black;">Shot</th>
<th colspan="3" rowspan="2" style="text-align: center; border-right: 1px solid black;">Overall Matching</th>
<th colspan="12" style="text-align: center;">F1 Score</th>
</tr>
<tr>
<th colspan="3" style="text-align: center; border-right: 1px solid black;">Overall</th>
<th colspan="3" style="text-align: center; border-right: 1px solid black;">Orientation</th>
<th colspan="3" style="text-align: center; border-right: 1px solid black;">Color</th>
<th colspan="3" style="text-align: center">Size</th>
</tr>
<tr>
<th style="text-align: center;">D</th>
<th style="text-align: center;">R</th>
<th style="border-right: 1px solid black;">VR</th>
<th style="text-align: center;">D</th>
<th style="text-align: center;">R</th>
<th style="border-right: 1px solid black;">VR</th>
<th style="text-align: center;">D</th>
<th style="text-align: center;">R</th>
<th style="border-right: 1px solid black;">VR</th>
<th style="text-align: center;">D</th>
<th style="text-align: center;">R</th>
<th style="border-right: 1px solid black;">VR</th>
<th style="text-align: center;">D</th>
<th style="text-align: center;">R</th>
<th style="text-align: center; border-right: 1px solid black;">VR</th>
</tr>
<tr>
<td>Claude-sonet</td>
<td style="border-right: 1px solid black;">0</td>
<td>86.4</td>
<td>89.0</td>
<td style="border-right: 1px solid black;">87.8</td>
<td>86.7</td>
<td>90.3</td>
<td style="border-right: 1px solid black;">87.7</td>
<td>83.4</td>
<td>87.6</td>
<td style="border-right: 1px solid black;">85.3</td>
<td>94.6</td>
<td>95.4</td>
<td style="border-right: 1px solid black;">95.5</td>
<td>82.0</td>
<td>87.9</td>
<td>82.2</td>
</tr>
<tr>
<td>NVLM-D-72B</td>
<td style="border-right: 1px solid black;">0</td>
<td>83.4</td>
<td >57.9</td>
<td style="border-right: 1px solid black;">59.8</td>
<td>83.2</td>
<td>73.7</td>
<td style="border-right: 1px solid black;">51.7</td>
<td>77.4</td>
<td>75.1</td>
<td style="border-right: 1px solid black;">61.8</td>
<td>98.0</td>
<td >80.2</td>
<td style="border-right: 1px solid black;">80.4</td>
<td>74.1</td>
<td>65.7</td>
<td>12.7</td>
</tr>
<tr>
<td>Molmo-7B</td>
<td style="border-right: 1px solid black;">0</td>
<td>71.3</td>
<td>45.4</td>
<td style="border-right: 1px solid black;">30.1</td>
<td>67.2</td>
<td>38.0</td>
<td style="border-right: 1px solid black;">28.4</td>
<td>40.8</td>
<td>62.3</td>
<td style="border-right: 1px solid black;">34.5</td>
<td>95.3</td>
<td>23.3</td>
<td style="border-right: 1px solid black;">15.7</td>
<td>69.3</td>
<td>28.5</td>
<td>22.3</td>
</tr>
<tr>
<td>Molmo-72B</td>
<td style="border-right: 1px solid black;">0</td>
<td>84.1</td>
<td>67.0</td>
<td style="border-right: 1px solid black;">75.5</td>
<td>83.4</td>
<td>65.6</td>
<td style="border-right: 1px solid black;">73.6</td>
<td>80.7</td>
<td>73.4</td>
<td style="border-right: 1px solid black;">77.5</td>
<td>96.5</td>
<td>69.4</td>
<td style="border-right: 1px solid black;">84.5</td>
<td>72.9</td>
<td>54.0</td>
<td>58.5</td>
</tr>
<tr>
<td>LLama3.2-Vision-11B</td>
<td style="border-right: 1px solid black;">0</td>
<td>51.4</td>
<td>17.6</td>
<td style="border-right: 1px solid black;">55.5</td>
<td>48.7</td>
<td>52.4</td>
<td style="border-right: 1px solid black;">52.4</td>
<td>52.6</td>
<td>57.9</td>
<td style="border-right: 1px solid black;">59.7</td>
<td>62.7</td>
<td>58.6</td>
<td style="border-right: 1px solid black;">69.7</td>
<td>30.9</td>
<td>40.7</td>
<td>27.8</td>
</tr>
<tr>
<td>PaliGemma-3B-448</td>
<td style="border-right: 1px solid black;">0</td>
<td>39.7</td>
<td>7.1</td>
<td style="border-right: 1px solid black;">2.4</td>
<td>41.4</td>
<td>9.5</td>
<td style="border-right: 1px solid black;">4.8</td>
<td>0.9</td>
<td>4.9</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>67.0</td>
<td>21.5</td>
<td style="border-right: 1px solid black;">2.8</td>
<td>55.1</td>
<td>2.0</td>
<td>11.7</td>
</tr>
<tr>
<td rowspan="3">Phi3-4B</td>
<td style="border-right: 1px solid black;">0</td>
<td>51.3</td>
<td>59.0</td>
<td style="border-right: 1px solid black;">52.1</td>
<td>41.2</td>
<td>55.3</td>
<td style="border-right: 1px solid black;">47.2</td>
<td>12.4</td>
<td>66.3</td>
<td style="border-right: 1px solid black;">45.9</td>
<td>45.3</td>
<td>50.5</td>
<td style="border-right: 1px solid black;">62.8</td>
<td>65.9</td>
<td>49.1</td>
<td>32.9</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>43.4</td>
<td>39.0</td>
<td style="border-right: 1px solid black;">47.1</td>
<td>33.5</td>
<td>27.1</td>
<td style="border-right: 1px solid black;">38.6</td>
<td>24.0</td>
<td>17.3</td>
<td style="border-right: 1px solid black;">5.8</td>
<td>26.5</td>
<td>54.9</td>
<td style="border-right: 1px solid black;">55.0</td>
<td>50.0</td>
<td>9.1</td>
<td>55.0</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>34.2</td>
<td>35.1</td>
<td style="border-right: 1px solid black;">50.8</td>
<td>17.0</td>
<td>18.9</td>
<td style="border-right: 1px solid black;">46.7</td>
<td>0.0</td>
<td>4.7</td>
<td style="border-right: 1px solid black;">34.5</td>
<td>51.0</td>
<td>51.6</td>
<td style="border-right: 1px solid black;">66.6</td>
<td>0.0</td>
<td>0.4</td>
<td>39.1</td>
</tr>
<tr>
<td rowspan="3">Phi3.5-Vision-3.5B</td>
<td style="border-right: 1px solid black;">0</td>
<td>44.0</td>
<td>59.9</td>
<td style="border-right: 1px solid black;">64.9</td>
<td>35.0</td>
<td>53.7</td>
<td style="border-right: 1px solid black;">63.6</td>
<td>2.1</td>
<td>53.7</td>
<td style="border-right: 1px solid black;">53.7</td>
<td>49.2</td>
<td>50.9</td>
<td style="border-right: 1px solid black;">71.3</td>
<td>53.7</td>
<td>56.6</td>
<td>65.9</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>26.7</td>
<td>49.8</td>
<td style="border-right: 1px solid black;">34.7</td>
<td>19.5</td>
<td>41.0</td>
<td style="border-right: 1px solid black;">20.8</td>
<td>0.0</td>
<td>0.5</td>
<td style="border-right: 1px solid black;">3.0</td>
<td>18.2</td>
<td>66.7</td>
<td style="border-right: 1px solid black;"`>9.9</td>
<td>40.3</td>
<td>55.8</td>
<td>49.5</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>35.2</td>
<td>24.1</td>
<td style="border-right: 1px solid black;">33.8</td>
<td>29.3</td>
<td>11.1</td>
<td style="border-right: 1px solid black;">19.0</td>
<td>1.5</td>
<td>0.2</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>38.9</td>
<td>26.0</td>
<td style="border-right: 1px solid black;">7.6</td>
<td>47.5</td>
<td>7.1</td>
<td>49.4</td>
</tr>
<tr>
<td rowspan="3">LLava 1.6-7B</td>
<td style="border-right: 1px solid black;">0</td>
<td>31.2</td>
<td>18.2</td>
<td style="border-right: 1px solid black;">17.7</td>
<td>16.3</td>
<td>10.1</td>
<td style="border-right: 1px solid black;">16.6</td>
<td>0.0</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>0.1</td>
<td>12.3</td>
<td style="border-right: 1px solid black;">49.9</td>
<td>48.9</td>
<td>18.1</td>
<td>0.0</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>32.4</td>
<td>17.7</td>
<td style="border-right: 1px solid black;">34.2</td>
<td>16.4</td>
<td>8.8</td>
<td style="border-right: 1px solid black;">17.0</td>
<td>0.0</td>
<td>1.4</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>0.0</td>
<td>10.1</td>
<td style="border-right: 1px solid black;">50.9</td>
<td>49.0</td>
<td>15.1</td>
<td>0.0</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>32.4</td>
<td>19.9</td>
<td style="border-right: 1px solid black;">34.2</td>
<td>16.4</td>
<td>9.1</td>
<td style="border-right: 1px solid black;">17.0</td>
<td>0.0</td>
<td>0.2</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>0.0</td>
<td>18.1</td>
<td style="border-right: 1px solid black;">50.9</td>
<td>49.0</td>
<td>9.1</td>
<td>0.0</td>
</tr>
<tr>
<td rowspan="3">Idefic2-8B</td>
<td style="border-right: 1px solid black;">0</td>
<td>64.5</td>
<td>45.2</td>
<td style="border-right: 1px solid black;">56.0</td>
<td>64.3</td>
<td>36.6</td>
<td style="border-right: 1px solid black;">49.5</td>
<td>62.9</td>
<td>51.1</td>
<td style="border-right: 1px solid black;">63.8</td>
<td>78.1</td>
<td>9.7</td>
<td style="border-right: 1px solid black;">64.1</td>
<td>51.9</td>
<td>49.2</td>
<td>20.5</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>66.9</td>
<td>42.6</td>
<td style="border-right: 1px solid black;">48.7</td>
<td>66.3</td>
<td>34.2</td>
<td style="border-right: 1px solid black;">39.5</td>
<td>66.6</td>
<td>9.7</td>
<td style="border-right: 1px solid black;">66.3</td>
<td>79.4</td>
<td>39.8</td>
<td style="border-right: 1px solid black;">9.5</td>
<td>53.0</td>
<td>53.1</td>
<td>9.7</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>66.7</td>
<td>49.6</td>
<td style="border-right: 1px solid black;">43.1</td>
<td>67.2</td>
<td>42.6</td>
<td style="border-right: 1px solid black;">34.5</td>
<td>65.3</td>
<td>8.6</td>
<td style="border-right: 1px solid black;">54.5</td>
<td>79.2</td>
<td>62.9</td>
<td style="border-right: 1px solid black;">11.9</td>
<td>57.2</td>
<td>56.3</td>
<td>37.0</td>
</tr>
<tr>
<td rowspan="3">Idefic3-8B</td>
<td style="border-right: 1px solid black;">0</td>
<td>40.2</td>
<td>58.3</td>
<td style="border-right: 1px solid black;">35.5</td>
<td>28.4</td>
<td>52.8</td>
<td style="border-right: 1px solid black;">19.2</td>
<td>24.1</td>
<td>54.9</td>
<td style="border-right: 1px solid black;">2.3</td>
<td>54.3</td>
<td>51.0</td>
<td style="border-right: 1px solid black;">49.7</td>
<td>6.9</td>
<td>52.5</td>
<td>5.5</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>50.9</td>
<td>35.9</td>
<td style="border-right: 1px solid black;">50.7</td>
<td>40.3</td>
<td>20.7</td>
<td style="border-right: 1px solid black;">40.6</td>
<td>0.5</td>
<td>0.5</td>
<td style="border-right: 1px solid black;">3.4</td>
<td>62.9</td>
<td>52.6</td>
<td style="border-right: 1px solid black;">63.6</td>
<td>57.6</td>
<td>8.9</td>
<td>54.8</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>36.3</td>
<td>34.5</td>
<td style="border-right: 1px solid black;">62.9</td>
<td>21.4</td>
<td>18.1</td>
<td style="border-right: 1px solid black;">58.3</td>
<td>0.0</td>
<td>0.2</td>
<td style="border-right: 1px solid black;">64.3</td>
<td>51.8</td>
<td>51.3</td>
<td style="border-right: 1px solid black;">85.7</td>
<td>12.3</td>
<td>2.7</td>
<td>25.0</td>
</tr>
<tr>
<td rowspan="3">VILA-1.5-8B</td>
<td style="border-right: 1px solid black;">0</td>
<td>34.2</td>
<td>30.4</td>
<td style="border-right: 1px solid black;">47.5</td>
<td>40.0</td>
<td>15.8</td>
<td style="border-right: 1px solid black;">17.0</td>
<td>17.6</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.5</td>
<td>56.3</td>
<td >28.8</td>
<td style="border-right: 1px solid black;">50.5</td>
<td>46.1</td>
<td>18.7</td>
<td>0.0</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>34.2</td>
<td>36.9</td>
<td style="border-right: 1px solid black;">34.2</td>
<td>17.0</td>
<td>28.8</td>
<td style="border-right: 1px solid black;">17.0</td>
<td>0.0</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.5</td>
<td>51.0</td>
<td>47.6</td>
<td style="border-right: 1px solid black;">50.5</td>
<td>0.0</td>
<td>38.5</td>
<td>0.0</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>34.2</td>
<td>39.5</td>
<td style="border-right: 1px solid black;">34.2</td>
<td>17.0</td>
<td>30.8</td>
<td style="border-right: 1px solid black;">17.0</td>
<td>0.0</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.5</td>
<td>51.0</td>
<td>51.3</td>
<td style="border-right: 1px solid black;">50.5</td>
<td>0.0</td>
<td>41.3</td>
<td>0.0</td>
</tr>
<tr>
<td rowspan="3">Qwen2-VL-2B</td>
<td style="border-right: 1px solid black;">0</td>
<td>30.3</td>
<td>34.5</td>
<td style="border-right: 1px solid black;">34.5</td>
<td>26.3</td>
<td>20.6</td>
<td style="border-right: 1px solid black;">20.2</td>
<td>14.5</td>
<td>5.0</td>
<td style="border-right: 1px solid black;">10.7</td>
<td>5.9</td>
<td>7.0</td>
<td style="border-right: 1px solid black;">1.6</td>
<td>58.3</td>
<td>49.8</td>
<td>49.6</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>35.7</td>
<td>35.3</td>
<td style="border-right: 1px solid black;">32.4</td>
<td>23.3</td>
<td>21.8</td>
<td style="border-right: 1px solid black;">16.3</td>
<td>0.0</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>17.5</td>
<td>15.2</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>53.8</td>
<td>50.1</td>
<td>49.0</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>35.3</td>
<td>32.6</td>
<td style="border-right: 1px solid black;">33.1</td>
<td>23.8</td>
<td>16.5</td>
<td style="border-right: 1px solid black;">17.7</td>
<td>0.0</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">4.1</td>
<td>15.2</td>
<td>0.7</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>54.6</td>
<td>49.0</td>
<td>49.3</td>
</tr>
<tr>
<td rowspan="3">Qwen2-VL-7B</td>
<td style="border-right: 1px solid black;">0</td>
<td>60.2</td>
<td>40.0</td>
<td style="border-right: 1px solid black;">59.9</td>
<td>55.7</td>
<td>34.2</td>
<td style="border-right: 1px solid black;">57.4</td>
<td>23.7</td>
<td>17.7</td>
<td style="border-right: 1px solid black;">53.6</td>
<td>82.0</td>
<td>45.0</td>
<td style="border-right: 1px solid black;">66.9</td>
<td>61.6</td>
<td>40.3</td>
<td>51.5</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>63.7</td>
<td>34.2</td>
<td style="border-right: 1px solid black;">69.8</td>
<td>53.8</td>
<td>17.0</td>
<td style="border-right: 1px solid black;">64.2</td>
<td>2.5</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">33.5</td>
<td>94.8</td>
<td>50.9</td>
<td style="border-right: 1px solid black;">84.9</td>
<td>64.1</td>
<td>0.0</td>
<td>74.0</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>64.5</td>
<td>34.2</td>
<td style="border-right: 1px solid black;">73.4</td>
<td>54.9</td>
<td>17.7</td>
<td style="border-right: 1px solid black;">72.0</td>
<td>4.5</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">56.3</td>
<td>95.6</td>
<td>50.9</td>
<td style="border-right: 1px solid black;">84.1</td>
<td>64.6</td>
<td>2.0</td>
<td>75.5</td>
</tr>
<tr>
<td rowspan="3">Qwen2-VL-72B</td>
<td style="border-right: 1px solid black;">0</td>
<td>89.1</td>
<td>93.6</td>
<td style="border-right: 1px solid black;">76.0</td>
<td>88.8</td>
<td>93.6</td>
<td style="border-right: 1px solid black;">74.7</td>
<td>85.2</td>
<td>91.3</td>
<td style="border-right: 1px solid black;">72.5</td>
<td>97.2</td>
<td>98.3</td>
<td style="border-right: 1px solid black;">86.0</td>
<td>83.9</td>
<td>91.1</td>
<td>65.7</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>89.3</td>
<td>93.1</td>
<td style="border-right: 1px solid black;">86.1</td>
<td>89.3</td>
<td>93.1</td>
<td style="border-right: 1px solid black;">85.9</td>
<td>86.7</td>
<td>90.4</td>
<td style="border-right: 1px solid black;">82.9</td>
<td>95.8</td>
<td>97.9</td>
<td style="border-right: 1px solid black;">96.2</td>
<td>85.5</td>
<td>91.1</td>
<td>78.8</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>89.2</td>
<td>92.7</td>
<td style="border-right: 1px solid black;">88.0</td>
<td>89.9</td>
<td>92.6</td>
<td style="border-right: 1px solid black;">87.9</td>
<td>88.3</td>
<td>90.0</td>
<td style="border-right: 1px solid black;">84.8</td>
<td>96.1</td>
<td>97.4</td>
<td style="border-right: 1px solid black;">96.5</td>
<td>85.4</td>
<td>90.5</td>
<td>82.3</td>
</tr>
<tr>
<td rowspan="3">InternVL-4B</td>
<td style="border-right: 1px solid black;">0</td>
<td>47.2</td>
<td>69.5</td>
<td style="border-right: 1px solid black;">58.9</td>
<td>41.5</td>
<td>63.4</td>
<td style="border-right: 1px solid black;">52.2</td>
<td>25.4</td>
<td>31.2</td>
<td style="border-right: 1px solid black;">67.2</td>
<td>64.5</td>
<td>88.2</td>
<td style="border-right: 1px solid black;">67.1</td>
<td>34.7</td>
<td>70.6</td>
<td>22.4</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>34.2</td>
<td>37.3</td>
<td style="border-right: 1px solid black;">49.9</td>
<td>17.0</td>
<td>25.3</td>
<td style="border-right: 1px solid black;">41.7</td>
<td>0.0</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">2.3</td>
<td>50.9</td>
<td>24.9</td>
<td style="border-right: 1px solid black;">66.5</td>
<td>0.0</td>
<td>50.9</td>
<td>56.5</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>34.2</td>
<td>48.0</td>
<td style="border-right: 1px solid black;">58.1</td>
<td>17.0</td>
<td>39.1</td>
<td style="border-right: 1px solid black;">52.5</td>
<td>0.0</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">61.7</td>
<td>50.9</td>
<td>61.4</td>
<td style="border-right: 1px solid black;">76.5</td>
<td>0.0</td>
<td>55.9</td>
<td>19.5</td>
</tr>
<tr>
<td rowspan="3">InternVL-8B</td>
<td style="border-right: 1px solid black;">0</td>
<td>65.6</td>
<td>74.2</td>
<td style="border-right: 1px solid black;"`>37.0</td>
<td>58.7</td>
<td>71.9</td>
<td style="border-right: 1px solid black;">23.0</td>
<td>66.9</td>
<td>50.4</td>
<td style="border-right: 1px solid black;">9.9</td>
<td>95.8</td>
<td>93.7</td>
<td style="border-right: 1px solid black;">52.0</td>
<td>13.4</td>
<td>71.5</td>
<td>7.1</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>60.6</td>
<td>61.7</td>
<td style="border-right: 1px solid black;">66.9</td>
<td>52.3</td>
<td>51.7</td>
<td style="border-right: 1px solid black;">64.4</td>
<td>7.4</td>
<td>1.6</td>
<td style="border-right: 1px solid black;">44.5</td>
<td>87.0</td>
<td>90.9</td>
<td style="border-right: 1px solid black;">85.7</td>
<td>62.6</td>
<td>62.4</td>
<td>63.0</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>51.0</td>
<td>62.5</td>
<td style="border-right: 1px solid black;">61.6</td>
<td>43.9</td>
<td>53.7</td>
<td style="border-right: 1px solid black;">50.5</td>
<td>15.6</td>
<td>8.6</td>
<td style="border-right: 1px solid black;">66.5</td>
<td>60.4</td>
<td>89.2</td>
<td style="border-right: 1px solid black;">83.6</td>
<td>55.6</td>
<td>63.3</td>
<td>1.4</td>
</tr>
<tr>
<td rowspan="3">GPT-4o</td>
<td style="border-right: 1px solid black;">0</td>
<td>89.2</td>
<td >88.7</td>
<td style="border-right: 1px solid black;">74.7</td>
<td>89.2</td>
<td>88.4</td>
<td style="border-right: 1px solid black;">73.5</td>
<td>86.3</td>
<td>85.2</td>
<td style="border-right: 1px solid black;">73.9</td>
<td>97.2</td>
<td>96.7</td>
<td style="border-right: 1px solid black;">94.6</td>
<td>84.0</td>
<td>83.5</td>
<td>52.0</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>87.7</td>
<td>88.0</td>
<td style="border-right: 1px solid black;">86.3</td>
<td>88.4</td>
<td>87.7</td>
<td style="border-right: 1px solid black;">86.7</td>
<td>85.8</td>
<td>84.7</td>
<td style="border-right: 1px solid black;">82.8</td>
<td>97.3</td>
<td>95.6</td>
<td style="border-right: 1px solid black;">95.8</td>
<td>82.8</td>
<td>82.7</td>
<td>81.4</td>
</tr>
<tr style="border-bottom: 2px solid black;">
<td style="border-right: 1px solid black;">5</td>
<td>86.0</td>
<td>89.0</td>
<td style="border-right: 1px solid black;">87.1</td>
<td>86.0</td>
<td>89.1</td>
<td style="border-right: 1px solid black;">87.4</td>
<td>82.8</td>
<td>85.3</td>
<td style="border-right: 1px solid black;">84.4</td>
<td>97.6</td>
<td>97.9</td>
<td style="border-right: 1px solid black;">95.7</td>
<td>77.5</td>
<td>84.1</td>
<td>82.0</td>
</tr>
</table>
#### + Exact Match and F1-Scores on the Realworld image set (**O3**) of SalBench.
<table>
<tr style="border-top: 2px solid black;">
<th rowspan="3" >Model</th>
<th rowspan="3" style="text-align: center; border-right: 1px solid black;">Shot</th>
<th colspan="3" rowspan="2" style="text-align: center; border-right: 1px solid black;">Overall Matching</th>
<th colspan="24" style="text-align: center;">F1 Score</th>
</tr>
<tr>
<th colspan="3" style="text-align: center; border-right: 1px solid black;">Overall</th>
<th colspan="3" style="text-align: center; border-right: 1px solid black;">Orientation</th>
<th colspan="3" style="text-align: center; border-right: 1px solid black;">Color</th>
<th colspan="3" style="text-align: center; border-right: 1px solid black;">Size</th>
<th colspan="3" style="text-align: center; border-right: 1px solid black;">Focus</th>
<th colspan="3" style="text-align: center; border-right: 1px solid black;">Shape</th>
<th colspan="3" style="text-align: center; border-right: 1px solid black;">Location</th>
<th colspan="3" style="text-align: center;">Pattern</th>
</tr>
<tr>
<th style="text-align: center">D</th>
<th style="text-align: center;">R</th>
<th style="text-align: center; border-right: 1px solid black;">VR</th>
<th style="text-align: center;">D</th>
<th style="text-align: center;">R</th>
<th style="text-align: center; border-right: 1px solid black;">VR</th>
<th style="text-align: center;">D</th>
<th style="text-align: center;">R</th>
<th style="text-align: center; border-right: 1px solid black;">VR</th>
<th style="text-align: center;">D</th>
<th style="text-align: center;">R</th>
<th style="text-align: center; border-right: 1px solid black;">VR</th>
<th style="text-align: center;">D</th>
<th style="text-align: center;">R</th>
<th style="text-align: center; border-right: 1px solid black;">VR</th>
<th style="text-align: center;">D</th>
<th style="text-align: center;">R</th>
<th style="text-align: center; border-right: 1px solid black;">VR</th>
<th style="text-align: center;">D</th>
<th style="text-align: center;">R</th>
<th style="text-align: center; border-right: 1px solid black;">VR</th>
<th style="text-align: center;">D</th>
<th style="text-align: center;">R</th>
<th style="text-align: center; border-right: 1px solid black;">VR</th>
<th style="text-align: center;">D</th>
<th style="text-align: center;">R</th>
<th style="text-align: center;">VR</th>
</tr>
<tr>
<td>Claude</td>
<td style="border-right: 1px solid black;">0</td>
<td>40.6</td>
<td>42.7</td>
<td style="border-right: 1px solid black;">40.3</td>
<td>48.2</td>
<td>51.1</td>
<td style="border-right: 1px solid black;">53.9</td>
<td>40.0</td>
<td>43.9</td>
<td style="border-right: 1px solid black;">49.2</td>
<td>95.2</td>
<td>95.9</td>
<td style="border-right: 1px solid black;">95.8</td>
<td>40.7</td>
<td>47.7</td>
<td style="border-right: 1px solid black;">44.1</td>
<td>27.6</td>
<td>14.9</td>
<td style="border-right: 1px solid black;">21.0</td>
<td>51.6</td>
<td>59.3</td>
<td style="border-right: 1px solid black;">60.4</td>
<td>28.7</td>
<td>34.0</td>
<td style="border-right: 1px solid black;">41.7</td>
<td>53.3</td>
<td>62.2</td>
<td>64.9</td>
</tr>
<tr>
<td>NVLM-D-72B</td>
<td style="border-right: 1px solid black;">0</td>
<td>26.7</td>
<td>35.6</td>
<td style="border-right: 1px solid black;">21.6</td>
<td>36.5</td>
<td>42.1</td>
<td style="border-right: 1px solid black;">37.3</td>
<td>36.6</td>
<td>35.1</td>
<td style="border-right: 1px solid black;">28.4</td>
<td>90.9</td>
<td>93.2</td>
<td style="border-right: 1px solid black;">89.4</td>
<td>28.6</td>
<td>36.0</td>
<td style="border-right: 1px solid black;">34.1</td>
<td>8.3</td>
<td>16.1</td>
<td style="border-right: 1px solid black;">12.3</td>
<td>41.4</td>
<td>49.0</td>
<td style="border-right: 1px solid black;">42.5</td>
<td>14.7</td>
<td>18.4</td>
<td style="border-right: 1px solid black;">8.3</td>
<td>34.8</td>
<td>47.1</td>
<td>45.9</td>
</tr>
<tr>
<td>Molmo-72B</td>
<td style="border-right: 1px solid black;">0</td>
<td>19.2</td>
<td>18.6</td>
<td style="border-right: 1px solid black;">15.6</td>
<td>40.6</td>
<td>41.2</td>
<td style="border-right: 1px solid black;">36.7</td>
<td>27.6</td>
<td>30.6</td>
<td style="border-right: 1px solid black;">24.1</td>
<td>94.0</td>
<td>91.8</td>
<td style="border-right: 1px solid black;">90.2</td>
<td>35.3</td>
<td>32.2</td>
<td style="border-right: 1px solid black;">30.1</td>
<td>17.0</td>
<td>14.2</td>
<td style="border-right: 1px solid black;">12.2</td>
<td>44.5</td>
<td>41.8</td>
<td style="border-right: 1px solid black;">39.2</td>
<td>12.5</td>
<td>18.3</td>
<td style="border-right: 1px solid black;">11.9</td>
<td>53.2</td>
<td>59.6</td>
<td>51.1</td>
</tr>
<tr>
<td>Molmo-7B</td>
<td style="border-right: 1px solid black;">0</td>
<td>2.5</td>
<td>8.9</td>
<td style="border-right: 1px solid black;">14.6</td>
<td>32.0</td>
<td>32.4</td>
<td style="border-right: 1px solid black;">33.0</td>
<td>15.2</td>
<td>18.6</td>
<td style="border-right: 1px solid black;">24.2</td>
<td>88.5</td>
<td>80.1</td>
<td style="border-right: 1px solid black;">88.2</td>
<td>34.8</td>
<td>38.8</td>
<td style="border-right: 1px solid black;">32.7</td>
<td>13.5</td>
<td>13.7</td>
<td style="border-right: 1px solid black;">10.8</td>
<td>33.2</td>
<td>40.1</td>
<td style="border-right: 1px solid black;">41.0</td>
<td>10.0</td>
<td>8.0</td>
<td style="border-right: 1px solid black;">7.7</td>
<td>28.8</td>
<td>27.0</td>
<td>29.9</td>
</tr>
<tr>
<td>Llama3.2-Vision-11B</td>
<td style="border-right: 1px solid black;">0</td>
<td>2.8</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>32.1</td>
<td>29.1</td>
<td style="border-right: 1px solid black;">29.7</td>
<td>17.7</td>
<td>17.1</td>
<td style="border-right: 1px solid black;">27.1</td>
<td>90.6</td>
<td>89.3</td>
<td style="border-right: 1px solid black;">85.6</td>
<td>31.1</td>
<td>33.4</td>
<td style="border-right: 1px solid black;">18.1</td>
<td>12.7</td>
<td>11.5</td>
<td style="border-right: 1px solid black;">9.3</td>
<td>37.5</td>
<td>44.6</td>
<td style="border-right: 1px solid black;">45.5</td>
<td>8.4</td>
<td>8.1</td>
<td style="border-right: 1px solid black;">22.5</td>
<td>20.6</td>
<td>0.0</td>
<td>0.0</td>
</tr>
<tr>
<td>PaliGemma-3B-448</td>
<td style="border-right: 1px solid black;">0</td>
<td>1.4</td>
<td>1.0</td>
<td style="border-right: 1px solid black;">0.7</td>
<td>27.6</td>
<td>1.2</td>
<td style="border-right: 1px solid black;">2.3</td>
<td>16.5</td>
<td>8.1</td>
<td style="border-right: 1px solid black;">13.6</td>
<td>84.3</td>
<td>0.7</td>
<td style="border-right: 1px solid black;">1.6</td>
<td>27.2</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>11.6</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>32.5</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>10.4</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>13.4</td>
<td>0.0</td>
<td>0.0</td>
</tr>
<tr>
<td rowspan="3">Phi3-4B</td>
<td style="border-right: 1px solid black;">0</td>
<td>7.0</td>
<td>4.5</td>
<td style="border-right: 1px solid black;">6.4</td>
<td>32.1</td>
<td>32.8</td>
<td style="border-right: 1px solid black;">32.8</td>
<td>2.1</td>
<td>2.1</td>
<td style="border-right: 1px solid black;">1.9</td>
<td>91.1</td>
<td>87.5</td>
<td style="border-right: 1px solid black;">88.2</td>
<td>25.2</td>
<td>29.3</td>
<td style="border-right: 1px solid black;">26.3</td>
<td>13.5</td>
<td>11.3</td>
<td style="border-right: 1px solid black;">14.3</td>
<td>40.2</td>
<td>42.1</td>
<td style="border-right: 1px solid black;">41.1</td>
<td>7.5</td>
<td>7.8</td>
<td style="border-right: 1px solid black;">7.4</td>
<td>45.2</td>
<td>43.9</td>
<td>49.6</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>0.0</td>
<td>1.7</td>
<td style="border-right: 1px solid black;">3.6</td>
<td>34.1</td>
<td>32.0</td>
<td style="border-right: 1px solid black;">32.1</td>
<td>15.5</td>
<td>14.9</td>
<td style="border-right: 1px solid black;">12.0</td>
<td>89.6</td>
<td>88.7</td>
<td style="border-right: 1px solid black;">88.1</td>
<td>30.6</td>
<td>29.2</td>
<td style="border-right: 1px solid black;">23.5</td>
<td>9.4</td>
<td>10.8</td>
<td style="border-right: 1px solid black;">11.1</td>
<td>40.3</td>
<td>38.9</td>
<td style="border-right: 1px solid black;">39.8</td>
<td>7.0</td>
<td>7.3</td>
<td style="border-right: 1px solid black;">8.3</td>
<td>46.5</td>
<td>34.8</td>
<td>42.2</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>0.0</td>
<td>1.2</td>
<td style="border-right: 1px solid black;">1.3</td>
<td>31.1</td>
<td>32.1</td>
<td style="border-right: 1px solid black;">32.2</td>
<td>16.6</td>
<td>14.3</td>
<td style="border-right: 1px solid black;">12.7</td>
<td>78.7</td>
<td>88.9</td>
<td style="border-right: 1px solid black;">89.1</td>
<td>28.9</td>
<td>31.2</td>
<td style="border-right: 1px solid black;">28.7</td>
<td>8.8</td>
<td>10.8</td>
<td style="border-right: 1px solid black;">7.1</td>
<td>38.3</td>
<td>32.1</td>
<td style="border-right: 1px solid black;">40.7</td>
<td>6.6</td>
<td>7.8</td>
<td style="border-right: 1px solid black;">7.7</td>
<td>41.3</td>
<td>39.1</td>
<td>39.8</td>
</tr>
<tr>
<td rowspan="3">Phi3.5-Vision-3.5B</td>
<td style="border-right: 1px solid black;">0</td>
<td>12.6</td>
<td>2.3</td>
<td style="border-right: 1px solid black;">7.3</td>
<td>23.2</td>
<td>27.5</td>
<td style="border-right: 1px solid black;">27.5</td>
<td>1.1</td>
<td>22.1</td>
<td style="border-right: 1px solid black;">12.7</td>
<td>91.1</td>
<td>86.2</td>
<td style="border-right: 1px solid black;">88.6</td>
<td>29.9</td>
<td>22.7</td>
<td style="border-right: 1px solid black;">22.6</td>
<td>4.8</td>
<td>11.8</td>
<td style="border-right: 1px solid black;">9.8</td>
<td>9.4</td>
<td>37.2</td>
<td style="border-right: 1px solid black;">39.1</td>
<td>1.4</td>
<td>7.9</td>
<td style="border-right: 1px solid black;">7.2</td>
<td>24.4</td>
<td>4.4</td>
<td>27.2</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>0.1</td>
<td>3.4</td>
<td style="border-right: 1px solid black;">9.2</td>
<td>23.3</td>
<td>28.8</td>
<td style="border-right: 1px solid black;">28.8</td>
<td>16.0</td>
<td>15.6</td>
<td style="border-right: 1px solid black;">13.5</td>
<td>58.8</td>
<td>89.6</td>
<td style="border-right: 1px solid black;">90.4</td>
<td>26.5</td>
<td>24.7</td>
<td style="border-right: 1px solid black;">25.5</td>
<td>9.8</td>
<td>9.7</td>
<td style="border-right: 1px solid black;">11.5</td>
<td>31.9</td>
<td>38.9</td>
<td style="border-right: 1px solid black;">39.2</td>
<td>6.9</td>
<td>7.2</td>
<td style="border-right: 1px solid black;">7.4</td>
<td>12.9</td>
<td>15.8</td>
<td>28.7</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>0.5</td>
<td>0.4</td>
<td style="border-right: 1px solid black;">10.3</td>
<td>25.2</td>
<td>30.8</td>
<td style="border-right: 1px solid black;">30.8</td>
<td>15.2</td>
<td>15.6</td>
<td style="border-right: 1px solid black;">8.7</td>
<td>52.5</td>
<td>90.2</td>
<td style="border-right: 1px solid black;">88.5</td>
<td>28.5</td>
<td>31.5</td>
<td style="border-right: 1px solid black;">21.2</td>
<td>8.9</td>
<td>8.8</td>
<td style="border-right: 1px solid black;">8.3</td>
<td>34.1</td>
<td>41.1</td>
<td style="border-right: 1px solid black;">40.9</td>
<td>7.3</td>
<td>7.8</td>
<td style="border-right: 1px solid black;">7.0</td>
<td>29.6</td>
<td>21.3</td>
<td>40.5</td>
</tr>
<tr>
<td rowspan="3">LLava 1.6-7B</td>
<td style="border-right: 1px solid black;">0</td>
<td>11.1</td>
<td>20.4</td>
<td style="border-right: 1px solid black;">22.8</td>
<td>24.6</td>
<td>21.4</td>
<td style="border-right: 1px solid black;">20.8</td>
<td>13.4</td>
<td>3.3</td>
<td style="border-right: 1px solid black;">1.1</td>
<td>91.1</td>
<td>72.4</td>
<td style="border-right: 1px solid black;">71.9</td>
<td>19.3</td>
<td>23.4</td>
<td style="border-right: 1px solid black;">22.8</td>
<td>10.9</td>
<td>8.5</td>
<td style="border-right: 1px solid black;">10.7</td>
<td>15.8</td>
<td>28.6</td>
<td style="border-right: 1px solid black;">22.9</td>
<td>8.9</td>
<td>4.5</td>
<td style="border-right: 1px solid black;">3.6</td>
<td>12.6</td>
<td>9.1</td>
<td>12.4</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>0.0</td>
<td>0.1</td>
<td style="border-right: 1px solid black;">0.2</td>
<td>7.1</td>
<td>15.2</td>
<td style="border-right: 1px solid black;">17.8</td>
<td>3.6</td>
<td>1.1</td>
<td style="border-right: 1px solid black;">5.2</td>
<td>10.4</td>
<td>15.2</td>
<td style="border-right: 1px solid black;">29.3</td>
<td>12.2</td>
<td>21.5</td>
<td style="border-right: 1px solid black;">20.8</td>
<td>4.3</td>
<td>10.3</td>
<td style="border-right: 1px solid black;">9.1</td>
<td>9.5</td>
<td>30.7</td>
<td style="border-right: 1px solid black;">32.7</td>
<td>5.4</td>
<td>8.4</td>
<td style="border-right: 1px solid black;">5.5</td>
<td>5.4</td>
<td>19.4</td>
<td>21.9</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>0.6</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>11.4</td>
<td>10.9</td>
<td style="border-right: 1px solid black;">9.7</td>
<td>0.0</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>24.1</td>
<td>4.3</td>
<td style="border-right: 1px solid black;">0.7</td>
<td>21.5</td>
<td>22.3</td>
<td style="border-right: 1px solid black;">20.1</td>
<td>5.5</td>
<td>7.1</td>
<td style="border-right: 1px solid black;">7.2</td>
<td>17.4</td>
<td>30.2</td>
<td style="border-right: 1px solid black;">27.9</td>
<td>5.6</td>
<td>7.7</td>
<td style="border-right: 1px solid black;">5.9</td>
<td>5.6</td>
<td>6.5</td>
<td>5.8</td>
</tr>
<tr>
<td rowspan="3">Idefics2-8B</td>
<td style="border-right: 1px solid black;">0</td>
<td>37.1</td>
<td>5.5</td>
<td style="border-right: 1px solid black;">4.2</td>
<td>19.5</td>
<td>29.6</td>
<td style="border-right: 1px solid black;">33.8</td>
<td>7.6</td>
<td>15.6</td>
<td style="border-right: 1px solid black;">11.9</td>
<td>91.9</td>
<td>72.5</td>
<td style="border-right: 1px solid black;">85.3</td>
<td>19.6</td>
<td>30.0</td>
<td style="border-right: 1px solid black;">32.8</td>
<td>0.4</td>
<td>11.6</td>
<td style="border-right: 1px solid black;">16.0</td>
<td>9.6</td>
<td>46.2</td>
<td style="border-right: 1px solid black;">44.7</td>
<td>5.4</td>
<td>7.5</td>
<td style="border-right: 1px solid black;">7.5</td>
<td>4.3</td>
<td>23.5</td>
<td>38.3</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>8.4</td>
<td>24.3</td>
<td style="border-right: 1px solid black;">8.7</td>
<td>21.1</td>
<td>28.4</td>
<td style="border-right: 1px solid black;">31.1</td>
<td>13.0</td>
<td>8.3</td>
<td style="border-right: 1px solid black;">11.5</td>
<td>62.3</td>
<td>88.7</td>
<td style="border-right: 1px solid black;">84.5</td>
<td>17.1</td>
<td>11.4</td>
<td style="border-right: 1px solid black;">21.7</td>
<td>13.5</td>
<td>12.2</td>
<td style="border-right: 1px solid black;">10.3</td>
<td>25.0</td>
<td>40.4</td>
<td style="border-right: 1px solid black;">40.8</td>
<td>5.8</td>
<td>7.2</td>
<td style="border-right: 1px solid black;">8.2</td>
<td>11.3</td>
<td>30.6</td>
<td>40.4</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>16.1</td>
<td>24.2</td>
<td style="border-right: 1px solid black;">10.5</td>
<td>34.7</td>
<td>28.3</td>
<td style="border-right: 1px solid black;">30.9</td>
<td>22.5</td>
<td>2.3</td>
<td style="border-right: 1px solid black;">2.1</td>
<td>88.0</td>
<td>90.5</td>
<td style="border-right: 1px solid black;">88.4</td>
<td>30.0</td>
<td>13.6</td>
<td style="border-right: 1px solid black;">23.7</td>
<td>11.8</td>
<td>10.0</td>
<td style="border-right: 1px solid black;">9.9</td>
<td>39.2</td>
<td>38.1</td>
<td style="border-right: 1px solid black;">43.0</td>
<td>8.6</td>
<td>6.9</td>
<td style="border-right: 1px solid black;">8.6</td>
<td>42.9</td>
<td>36.6</td>
<td>40.8</td>
</tr>
<tr>
<td rowspan="3">Idefics3-8B</td>
<td style="border-right: 1px solid black;">0</td>
<td>16.1</td>
<td>20.7</td>
<td style="border-right: 1px solid black;">17.1</td>
<td>24.3</td>
<td>24.3</td>
<td style="border-right: 1px solid black;">22.1</td>
<td>0.0</td>
<td>5.0</td>
<td style="border-right: 1px solid black;">2.3</td>
<td>91.5</td>
<td>90.7</td>
<td style="border-right: 1px solid black;">91.6</td>
<td>38.5</td>
<td>35.0</td>
<td style="border-right: 1px solid black;">9.3</td>
<td>11.0</td>
<td>11.1</td>
<td style="border-right: 1px solid black;">4.5</td>
<td>5.8</td>
<td>6.0</td>
<td style="border-right: 1px solid black;">32.9</td>
<td>6.2</td>
<td>5.0</td>
<td style="border-right: 1px solid black;">9.1</td>
<td>17.2</td>
<td>18.0</td>
<td>5.0</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>8.7</td>
<td>10.1</td>
<td style="border-right: 1px solid black;">6.2</td>
<td>26.9</td>
<td>26.9</td>
<td style="border-right: 1px solid black;">21.9</td>
<td>8.1</td>
<td>7.5</td>
<td style="border-right: 1px solid black;">1.1</td>
<td>84.0</td>
<td>86.4</td>
<td style="border-right: 1px solid black;">90.6</td>
<td>22.2</td>
<td>23.0</td>
<td style="border-right: 1px solid black;">5.8</td>
<td>13.1</td>
<td>12.0</td>
<td style="border-right: 1px solid black;">11.9</td>
<td>32.2</td>
<td>31.0</td>
<td style="border-right: 1px solid black;">38.9</td>
<td>7.0</td>
<td>6.5</td>
<td style="border-right: 1px solid black;">4.5</td>
<td>21.8</td>
<td>22.0</td>
<td>0.6</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>4.4</td>
<td>9.0</td>
<td style="border-right: 1px solid black;">5.4</td>
<td>22.3</td>
<td>26.9</td>
<td style="border-right: 1px solid black;">20.9</td>
<td>5.5</td>
<td>8.5</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>65.1</td>
<td>88.3</td>
<td style="border-right: 1px solid black;">90.7</td>
<td>15.1</td>
<td>17.5</td>
<td style="border-right: 1px solid black;">3.5</td>
<td>15.1</td>
<td>14.8</td>
<td style="border-right: 1px solid black;">6.4</td>
<td>27.6</td>
<td>28.0</td>
<td style="border-right: 1px solid black;">39.8</td>
<td>5.4</td>
<td>8.7</td>
<td style="border-right: 1px solid black;">5.6</td>
<td>22.7</td>
<td>22.5</td>
<td>0.0</td>
</tr>
<tr>
<td rowspan="3">VILA-1.5-8B</td>
<td style="border-right: 1px solid black;">0</td>
<td>3.8</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>23.5</td>
<td>13.0</td>
<td style="border-right: 1px solid black;">15.8</td>
<td>0.0</td>
<td>6.2</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>85.2</td>
<td>19.2</td>
<td style="border-right: 1px solid black;">27.1</td>
<td>31.8</td>
<td>21.1</td>
<td style="border-right: 1px solid black;">27.3</td>
<td>1.6</td>
<td>3.1</td>
<td style="border-right: 1px solid black;">8.1</td>
<td>35.4</td>
<td>34.8</td>
<td style="border-right: 1px solid black;">36.6</td>
<td>8.8</td>
<td>4.9</td>
<td style="border-right: 1px solid black;">9.1</td>
<td>1.8</td>
<td>2.1</td>
<td >2.7</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>1.2</td>
<td>0.8</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>25.1</td>
<td>28.8</td>
<td style="border-right: 1px solid black;">28.8</td>
<td>16.6</td>
<td>11.6</td>
<td style="border-right: 1px solid black;">6.0</td>
<td>68.3</td>
<td>72.4</td>
<td style="border-right: 1px solid black;">79.5</td>
<td>22.1</td>
<td>31.0</td>
<td style="border-right: 1px solid black;">28.3</td>
<td>9.7</td>
<td>10.7</td>
<td style="border-right: 1px solid black;">9.1</td>
<td>24.9</td>
<td>35.5</td>
<td style="border-right: 1px solid black;">36.5</td>
<td>8.9</td>
<td>7.2</td>
<td style="border-right: 1px solid black;">7.2</td>
<td>25.5</td>
<td>22.3</td>
<td>36.8</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>0.4</td>
<td>5.0</td>
<td style="border-right: 1px solid black;">6.0</td>
<td>23.2</td>
<td>30.8</td>
<td style="border-right: 1px solid black;">30.8</td>
<td>18.2</td>
<td>19.0</td>
<td style="border-right: 1px solid black;">18.0</td>
<td>59.5</td>
<td>74.6</td>
<td style="border-right: 1px solid black;">76.4</td>
<td>24.7</td>
<td>35.0</td>
<td style="border-right: 1px solid black;">32.0</td>
<td>11.6</td>
<td>14.1</td>
<td style="border-right: 1px solid black;">12.0</td>
<td>28.6</td>
<td>40.0</td>
<td style="border-right: 1px solid black;">38.0</td>
<td>8.3</td>
<td>7.0</td>
<td style="border-right: 1px solid black;">8.0</td>
<td>11.8</td>
<td>25.0</td>
<td>25.0</td>
</tr>
<tr>
<td rowspan="3">Qwen2-VL-2B</td>
<td style="border-right: 1px solid black;">0</td>
<td>34.1</td>
<td>4.6</td>
<td style="border-right: 1px solid black;">5.0</td>
<td>19.2</td>
<td>22.1</td>
<td style="border-right: 1px solid black;">20.9</td>
<td>25.7</td>
<td>19.0</td>
<td style="border-right: 1px solid black;">17.9</td>
<td>90.2</td>
<td>90.8</td>
<td style="border-right: 1px solid black;">91.2</td>
<td>18.2</td>
<td>8.3</td>
<td style="border-right: 1px solid black;">3.5</td>
<td>0.0</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>0.0</td>
<td>26.0</td>
<td style="border-right: 1px solid black;">31.0</td>
<td>0.0</td>
<td>8.3</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>0.3</td>
<td>2.1</td>
<td>2.4</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>4.8</td>
<td>18.9</td>
<td style="border-right: 1px solid black;">3.5</td>
<td>25.2</td>
<td>21.4</td>
<td style="border-right: 1px solid black;">20.2</td>
<td>7.7</td>
<td>17.5</td>
<td style="border-right: 1px solid black;">15.0</td>
<td>87.2</td>
<td>90.3</td>
<td style="border-right: 1px solid black;">90.5</td>
<td>27.9</td>
<td>2.9</td>
<td style="border-right: 1px solid black;">2.4</td>
<td>0.0</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>38.8</td>
<td>34.5</td>
<td style="border-right: 1px solid black;">33.7</td>
<td>5.9</td>
<td>3.4</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>8.5</td>
<td>0.9</td>
<td>0.0</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>2.7</td>
<td>26.3</td>
<td style="border-right: 1px solid black;">25.9</td>
<td>25.3</td>
<td>21.7</td>
<td style="border-right: 1px solid black;">20.9</td>
<td>15.8</td>
<td>19.0</td>
<td style="border-right: 1px solid black;">18.7</td>
<td>90.3</td>
<td>90.5</td>
<td style="border-right: 1px solid black;">90.3</td>
<td>28.1</td>
<td>11.8</td>
<td style="border-right: 1px solid black;">6.8</td>
<td>0.0</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>34.4</td>
<td>27.8</td>
<td style="border-right: 1px solid black;">24.6</td>
<td>3.0</td>
<td>2.2</td>
<td style="border-right: 1px solid black;">0.0</td>
<td>5.4</td>
<td>0.3</td>
<td>0.0</td>
</tr>
<tr>
<td rowspan="3">Qwen2-VL-7B</td>
<td style="border-right: 1px solid black;">0</td>
<td>9.1</td>
<td>10.2</td>
<td style="border-right: 1px solid black;">7.0</td>
<td>32.5</td>
<td>32.5</td>
<td style="border-right: 1px solid black;">35.2</td>
<td>31.0</td>
<td>30.1</td>
<td style="border-right: 1px solid black;">17.5</td>
<td>92.1</td>
<td>92.0</td>
<td style="border-right: 1px solid black;">91.5</td>
<td>32.3</td>
<td>33.5</td>
<td style="border-right: 1px solid black;">34.5</td>
<td>2.4</td>
<td>2.7</td>
<td style="border-right: 1px solid black;">3.8</td>
<td>32.1</td>
<td>36.4</td>
<td style="border-right: 1px solid black;">41.9</td>
<td>7.5</td>
<td>7.9</td>
<td style="border-right: 1px solid black;">10.5</td>
<td>32.3</td>
<td>33.2</td>
<td >46.7</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>2.8</td>
<td>4.0</td>
<td style="border-right: 1px solid black;">2.1</td>
<td>35.6</td>
<td>36.0</td>
<td style="border-right: 1px solid black;">34.1</td>
<td>22.4</td>
<td>25.3</td>
<td style="border-right: 1px solid black;">14.7</td>
<td>90.4</td>
<td>92.5</td>
<td style="border-right: 1px solid black;">91.1</td>
<td>33.1</td>
<td>34.5</td>
<td style="border-right: 1px solid black;">30.4</td>
<td>14.7</td>
<td>15.0</td>
<td style="border-right: 1px solid black;">10.7</td>
<td>42.8</td>
<td>41.0</td>
<td style="border-right: 1px solid black;">41.3</td>
<td>8.4</td>
<td>11.2</td>
<td style="border-right: 1px solid black;">9.0</td>
<td>37.8</td>
<td>38.6</td>
<td>41.6</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>2.0</td>
<td>2.1</td>
<td style="border-right: 1px solid black;">3.2</td>
<td>37.2</td>
<td>37.2</td>
<td style="border-right: 1px solid black;">29.3</td>
<td>24.6</td>
<td>22.0</td>
<td style="border-right: 1px solid black;">10.0</td>
<td>91.2</td>
<td>91.5</td>
<td style="border-right: 1px solid black;">91.1</td>
<td>32.3</td>
<td>32.0</td>
<td style="border-right: 1px solid black;">31.6</td>
<td>13.8</td>
<td>11.2</td>
<td style="border-right: 1px solid black;">4.9</td>
<td>32.3</td>
<td>43.0</td>
<td style="border-right: 1px solid black;">40.9</td>
<td>8.3</td>
<td>9.5</td>
<td style="border-right: 1px solid black;">9.7</td>
<td>47.8</td>
<td>43.5</td>
<td>16.8</td>
</tr>
<tr>
<td rowspan="3">Qwen2-VL-72B</td>
<td style="border-right: 1px solid black;">0</td>
<td>14.3</td>
<td>16.7</td>
<td style="border-right: 1px solid black;">14.3</td>
<td>41.7</td>
<td>44.6</td>
<td style="border-right: 1px solid black;">41.7</td>
<td>23.7</td>
<td>30.0</td>
<td style="border-right: 1px solid black;">23.7</td>
<td>93.7</td>
<td>94.8</td>
<td style="border-right: 1px solid black;">93.7</td>
<td>39.0</td>
<td>42.3</td>
<td style="border-right: 1px solid black;">39.0</td>
<td>12.8</td>
<td>19.8</td>
<td style="border-right: 1px solid black;">12.8</td>
<td>47.2</td>
<td>51.0</td>
<td style="border-right: 1px solid black;">47.2</td>
<td>13.4</td>
<td>13.2</td>
<td style="border-right: 1px solid black;">13.4</td>
<td>61.9</td>
<td>61.0</td>
<td>61.9</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>28.2</td>
<td>34.2</td>
<td style="border-right: 1px solid black;">28.2</td>
<td>43.9</td>
<td>43.6</td>
<td style="border-right: 1px solid black;">43.2</td>
<td>24.8</td>
<td>28.3</td>
<td style="border-right: 1px solid black;">24.8</td>
<td>93.1</td>
<td>94.1</td>
<td style="border-right: 1px solid black;">93.1</td>
<td>38.0</td>
<td>39.4</td>
<td style="border-right: 1px solid black;">37.9</td>
<td>18.9</td>
<td>16.0</td>
<td style="border-right: 1px solid black;">18.9</td>
<td>48.1</td>
<td>53.1</td>
<td style="border-right: 1px solid black;">48.1</td>
<td>23.1</td>
<td>17.6</td>
<td style="border-right: 1px solid black;">23.1</td>
<td>56.7</td>
<td>57.1</td>
<td>56.7</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>39.5</td>
<td>31.0</td>
<td style="border-right: 1px solid black;">27.0</td>
<td>43.9</td>
<td>44.9</td>
<td style="border-right: 1px solid black;">42.3</td>
<td>27.0</td>
<td>29.7</td>
<td style="border-right: 1px solid black;">21.6</td>
<td>93.7</td>
<td>94.7</td>
<td style="border-right: 1px solid black;">93.1</td>
<td>41.9</td>
<td>43.9</td>
<td style="border-right: 1px solid black;">35.8</td>
<td>15.5</td>
<td>13.1</td>
<td style="border-right: 1px solid black;">19.8</td>
<td>58.2</td>
<td>54.2</td>
<td style="border-right: 1px solid black;">49.3</td>
<td>20.2</td>
<td>20.0</td>
<td style="border-right: 1px solid black;">21.2</td>
<td>50.8</td>
<td>58.8</td>
<td>55.4</td>
</tr>
<tr>
<td rowspan="3">InternVL-4B</td>
<td style="border-right: 1px solid black;">0</td>
<td>14.9</td>
<td>4.6</td>
<td style="border-right: 1px solid black;">4.5</td>
<td>26.6</td>
<td>29.8</td>
<td style="border-right: 1px solid black;">30.7</td>
<td>0.0</td>
<td>10.5</td>
<td style="border-right: 1px solid black;">15.4</td>
<td>91.4</td>
<td>90.3</td>
<td style="border-right: 1px solid black;">91.4</td>
<td>14.3</td>
<td>25.3</td>
<td style="border-right: 1px solid black;">22.4</td>
<td>6.3</td>
<td>11.7</td>
<td style="border-right: 1px solid black;">9.3</td>
<td>41.8</td>
<td>41.0</td>
<td style="border-right: 1px solid black;">41.0</td>
<td>8.0</td>
<td>10.7</td>
<td style="border-right: 1px solid black;">12.2</td>
<td>24.6</td>
<td>19.4</td>
<td>23.4</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>4.1</td>
<td>2.2</td>
<td style="border-right: 1px solid black;">2.3</td>
<td>27.7</td>
<td>27.4</td>
<td style="border-right: 1px solid black;">29.5</td>
<td>16.3</td>
<td>15.8</td>
<td style="border-right: 1px solid black;">16.3</td>
<td>78.0</td>
<td>85.2</td>
<td style="border-right: 1px solid black;">89.3</td>
<td>25.7</td>
<td>26.5</td>
<td style="border-right: 1px solid black;">25.0</td>
<td>8.8</td>
<td>8.8</td>
<td style="border-right: 1px solid black;">10.0</td>
<td>36.7</td>
<td>33.9</td>
<td style="border-right: 1px solid black;">36.1</td>
<td>2.6</td>
<td>6.5</td>
<td style="border-right: 1px solid black;">7.6</td>
<td>26.0</td>
<td>14.9</td>
<td>22.0</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>3.2</td>
<td>1.6</td>
<td style="border-right: 1px solid black;">2.4</td>
<td>33.4</td>
<td>28.1</td>
<td style="border-right: 1px solid black;">30.4</td>
<td>16.9</td>
<td>15.4</td>
<td style="border-right: 1px solid black;">17.5</td>
<td>90.1</td>
<td>87.2</td>
<td style="border-right: 1px solid black;">90.4</td>
<td>26.8</td>
<td>27.6</td>
<td style="border-right: 1px solid black;">27.9</td>
<td>10.0</td>
<td>7.4</td>
<td style="border-right: 1px solid black;">7.8</td>
<td>40.1</td>
<td>37.9</td>
<td style="border-right: 1px solid black;">39.7</td>
<td>9.3</td>
<td>8.0</td>
<td style="border-right: 1px solid black;">9.2</td>
<td>40.9</td>
<td>13.1</td>
<td >20.5</td>
</tr>
<tr>
<td rowspan="3">InternVL-8B</td>
<td style="border-right: 1px solid black;">0</td>
<td>7.4</td>
<td>32.8</td>
<td style="border-right: 1px solid black;">37.4</td>
<td>20.0</td>
<td>23.0</td>
<td style="border-right: 1px solid black;">24.8</td>
<td>1.2</td>
<td>6.7</td>
<td style="border-right: 1px solid black;">2.2</td>
<td>92.3</td>
<td>90.2</td>
<td style="border-right: 1px solid black;">91.3</td>
<td>3.6</td>
<td>12.4</td>
<td style="border-right: 1px solid black;">18.2</td>
<td>12.4</td>
<td>6.8</td>
<td style="border-right: 1px solid black;">7.2</td>
<td>8.7</td>
<td>18.0</td>
<td style="border-right: 1px solid black;">22.0</td>
<td>16.2</td>
<td>11.4</td>
<td style="border-right: 1px solid black;">7.2</td>
<td>5.5</td>
<td>15.8</td>
<td>25.6</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>9.7</td>
<td>23.8</td>
<td style="border-right: 1px solid black;">5.8</td>
<td>30.5</td>
<td>24.2</td>
<td style="border-right: 1px solid black;">31.7</td>
<td>14.5</td>
<td>11.9</td>
<td style="border-right: 1px solid black;">13.9</td>
<td>80.5</td>
<td>89.0</td>
<td style="border-right: 1px solid black;">90.9</td>
<td>27.6</td>
<td>9.1</td>
<td style="border-right: 1px solid black;">25.1</td>
<td>9.9</td>
<td>13.3</td>
<td style="border-right: 1px solid black;">10.4</td>
<td>33.8</td>
<td>16.2</td>
<td style="border-right: 1px solid black;">35.4</td>
<td>7.2</td>
<td>0.0</td>
<td style="border-right: 1px solid black;">5.2</td>
<td>39.8</td>
<td>30.0</td>
<td>40.9</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">5</td>
<td>7.7</td>
<td>23.0</td>
<td style="border-right: 1px solid black;">6.7</td>
<td>27.8</td>
<td>25.0</td>
<td style="border-right: 1px solid black;">31.4</td>
<td>15.8</td>
<td>6.4</td>
<td style="border-right: 1px solid black;">11.6</td>
<td>79.6</td>
<td>90.7</td>
<td style="border-right: 1px solid black;">91.1</td>
<td>26.4</td>
<td>11.6</td>
<td style="border-right: 1px solid black;">27.8</td>
<td>10.8</td>
<td>6.8</td>
<td style="border-right: 1px solid black;">7.0</td>
<td>28.5</td>
<td>22.7</td>
<td style="border-right: 1px solid black;">37.8</td>
<td>7.7</td>
<td>2.2</td>
<td style="border-right: 1px solid black;">4.1</td>
<td>25.8</td>
<td>34.6</td>
<td>40.5</td>
</tr>
<tr>
<td rowspan="3">GPT-4o</td>
<td style="border-right: 1px solid black;">0</td>
<td>45.2</td>
<td>46.5</td>
<td style="border-right: 1px solid black;">42.9</td>
<td>47.6</td>
<td>47.3</td>
<td style="border-right: 1px solid black;">42.6</td>
<td>51.7</td>
<td>52.8</td>
<td style="border-right: 1px solid black;">48.7</td>
<td>95.5</td>
<td>95.7</td>
<td style="border-right: 1px solid black;">94.6</td>
<td>32.9</td>
<td>28.0</td>
<td style="border-right: 1px solid black;">14.1</td>
<td>30.2</td>
<td>19.3</td>
<td style="border-right: 1px solid black;">21.9</td>
<td>52.4</td>
<td>49.9</td>
<td style="border-right: 1px solid black;">42.3</td>
<td>35.6</td>
<td>40.3</td>
<td style="border-right: 1px solid black;">34.5</td>
<td>34.8</td>
<td>45.2</td>
<td>42.2</td>
</tr>
<tr>
<td style="border-right: 1px solid black;">3</td>
<td>42.8</td>
<td>39.8</td>
<td style="border-right: 1px solid black;">30.2</td>
<td>38.9</td>
<td>37.5</td>
<td style="border-right: 1px solid black;">35.7</td>
<td>49.8</td>
<td>33.7</td>
<td style="border-right: 1px solid black;">32.9</td>
<td>93.8</td>
<td>92.9</td>
<td style="border-right: 1px solid black;">87.0</td>
<td>21.9</td>
<td>21.7</td>
<td style="border-right: 1px solid black;">15.6</td>
<td>10.8</td>
<td>3.5</td>
<td style="border-right: 1px solid black;">11.6</td>
<td>46.2</td>
<td>44.4</td>
<td style="border-right: 1px solid black;">41.3</td>
<td>27.9</td>
<td>30.2</td>
<td style="border-right: 1px solid black;">20.8</td>
<td>28.7</td>
<td>42.3</td>
<td>41.1</td>
</tr>
<tr style="border-bottom: 2px solid black;">
<td style="border-right: 1px solid black;">5</td>
<td>43.4</td>
<td>42.3</td>
<td style="border-right: 1px solid black;">30.7</td>
<td>41.9</td>
<td>39.8</td>
<td style="border-right: 1px solid black;">38.4</td>
<td>46.8</td>
<td>42.6</td>
<td style="border-right: 1px solid black;">40.3</td>
<td>94.2</td>
<td>94.2</td>
<td style="border-right: 1px solid black;">87.4</td>
<td>28.9</td>
<td>19.2</td>
<td style="border-right: 1px solid black;">14.9</td>
<td>10.7</td>
<td>9.5</td>
<td style="border-right: 1px solid black;">20.3</td>
<td>47.6</td>
<td>44.9</td>
<td style="border-right: 1px solid black;">40.6</td>
<td>29.6</td>
<td>31.2</td>
<td style="border-right: 1px solid black;">26.1</td>
<td>35.2</td>
<td>37.2</td>
<td>39.1</td>
</tr>
</table>
## Examples
Some zero-shot and few-shot examples on different tasks and different image set can be found as following:
<p align="center">
<img src="./images/p3_4.png" width="80%" alt="Image 1">
</p>
<p align="center">
<img src="./images/p3_5.png" width="80%" alt="Image 2">
</p>
<p align="center">
<img src="./images/o3_4.png" width="80%" alt="Image 3">
</p>
<p align="center">
<img src="./images/o3_5.png" width="80%" alt="Image 4">
</p>
<p align="center">
<img src="./images/p3_2.png" width="80%" alt="Image 5">
</p>
<p align="center">
<img src="./images/p3_3.png" width="80%" alt="Image 6">
</p>
<p align="center">
<img src="./images/o3_1.png" width="80%" alt="Image 7">
</p>
<p align="center">
<img src="./images/o3_3.png" width="80%" alt="Image 8">
</p> |
DT4LM/t5v1-1base_rte_kuleshov_var_original_old | DT4LM | "2025-01-03T10:46:08Z" | 32 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-01-03T10:46:05Z" | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int32
splits:
- name: train
num_bytes: 42955
num_examples: 132
download_size: 33128
dataset_size: 42955
---
# Dataset Card for "t5v1-1base_rte_kuleshov_var_original_old"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mootez/cohesion-dataset | mootez | "2025-01-03T10:54:35Z" | 32 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
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"library:dask",
"library:mlcroissant",
"library:polars",
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] | null | "2025-01-03T10:54:20Z" | ---
dataset_info:
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splits:
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download_size: 134153790
dataset_size: 3232574364
configs:
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path: data/train-*
---
|
DT4LM/t5v1-1base_mrpc_pair_kuleshov_var | DT4LM | "2025-01-03T11:14:47Z" | 32 | 0 | [
"size_categories:n<1K",
"format:parquet",
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"library:pandas",
"library:mlcroissant",
"library:polars",
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] | null | "2025-01-03T11:09:16Z" | ---
dataset_info:
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype: int32
splits:
- name: train
num_bytes: 186168
num_examples: 734
download_size: 136122
dataset_size: 186168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
dgambettavuw/D_gen4_run2_llama2-7b_xlsum_doc1000_real64_synt64_vuw | dgambettavuw | "2025-01-03T11:51:31Z" | 32 | 0 | [
"size_categories:1K<n<10K",
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"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-01-03T11:51:28Z" | ---
dataset_info:
features:
- name: id
dtype: int64
- name: doc
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download_size: 374183
dataset_size: 605277
configs:
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path: data/train-*
---
|
MedialabGrup/Fibranova | MedialabGrup | "2025-01-03T12:18:50Z" | 32 | 0 | [
"license:mit",
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] | null | "2025-01-03T12:14:52Z" | ---
license: mit
---
|
cl3ms/awelito_dataset | cl3ms | "2025-01-03T13:56:57Z" | 32 | 0 | [
"size_categories:n<1K",
"format:parquet",
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"library:pandas",
"library:mlcroissant",
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] | null | "2025-01-03T13:56:55Z" | ---
dataset_info:
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configs:
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path: data/test-*
---
|
mytestdpo/llama3_star_plus_8b_gsm8k_kumar_baselinetmp07 | mytestdpo | "2025-01-03T15:49:52Z" | 32 | 0 | [
"size_categories:1K<n<10K",
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"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-01-03T15:49:51Z" | ---
dataset_info:
features:
- name: idx
dtype: int64
- name: gt
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download_size: 2674060
dataset_size: 8542046
configs:
- config_name: default
data_files:
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path: data/train-*
---
|
zekeZZ/gpqa_bio | zekeZZ | "2025-01-03T16:00:06Z" | 32 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-01-03T16:00:05Z" | ---
dataset_info:
features:
- name: Subdomain
dtype: string
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dtype: string
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sequence: string
- name: answer
dtype: int64
splits:
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num_examples: 78
download_size: 51782
dataset_size: 53494.59375
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Lots-of-LoRAs/task375_classify_type_of_sentence_in_debate | Lots-of-LoRAs | "2025-01-03T17:46:34Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-03T17:46:32Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task375_classify_type_of_sentence_in_debate
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 320
- name: valid
num_examples: 40
- name: test
num_examples: 40
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task375_classify_type_of_sentence_in_debate
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
Lots-of-LoRAs/task1705_ljspeech_classification | Lots-of-LoRAs | "2025-01-03T17:47:24Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-03T17:47:23Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task1705_ljspeech_classification
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 80
- name: valid
num_examples: 10
- name: test
num_examples: 10
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1705_ljspeech_classification
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
Lots-of-LoRAs/task1224_ted_translation_ja_ar | Lots-of-LoRAs | "2025-01-03T17:51:03Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-03T17:51:01Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task1224_ted_translation_ja_ar
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 5143
- name: valid
num_examples: 643
- name: test
num_examples: 643
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1224_ted_translation_ja_ar
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
Lots-of-LoRAs/task561_alt_translation_en_bg | Lots-of-LoRAs | "2025-01-03T17:52:16Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-03T17:52:14Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task561_alt_translation_en_bg
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 480
- name: valid
num_examples: 60
- name: test
num_examples: 60
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task561_alt_translation_en_bg
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
Lots-of-LoRAs/task880_schema_guided_dstc8_classification | Lots-of-LoRAs | "2025-01-03T17:56:45Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-03T17:56:42Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task880_schema_guided_dstc8_classification
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 1072
- name: valid
num_examples: 134
- name: test
num_examples: 135
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task880_schema_guided_dstc8_classification
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
Lots-of-LoRAs/task530_europarl_en_es_translation | Lots-of-LoRAs | "2025-01-03T18:02:38Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-03T18:02:35Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task530_europarl_en_es_translation
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 5133
- name: valid
num_examples: 642
- name: test
num_examples: 642
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task530_europarl_en_es_translation
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
Lots-of-LoRAs/task251_spl_translation_en_fi | Lots-of-LoRAs | "2025-01-03T18:03:54Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-03T18:03:53Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task251_spl_translation_en_fi
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 287
- name: valid
num_examples: 36
- name: test
num_examples: 36
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task251_spl_translation_en_fi
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
Lots-of-LoRAs/task1611_xquad_es_question_generation | Lots-of-LoRAs | "2025-01-03T18:11:48Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-03T18:11:46Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task1611_xquad_es_question_generation
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 192
- name: valid
num_examples: 24
- name: test
num_examples: 24
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1611_xquad_es_question_generation
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
Lots-of-LoRAs/task1280_ted_translation_pt_it | Lots-of-LoRAs | "2025-01-03T18:24:05Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-03T18:24:03Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task1280_ted_translation_pt_it
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 5112
- name: valid
num_examples: 639
- name: test
num_examples: 640
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1280_ted_translation_pt_it
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
Lots-of-LoRAs/task1153_bard_analogical_reasoning_affordance | Lots-of-LoRAs | "2025-01-03T18:35:07Z" | 32 | 0 | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2204.07705",
"arxiv:2407.00066",
"region:us"
] | [
"text-generation"
] | "2025-01-03T18:35:05Z" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
task_categories:
- text-generation
pretty_name: task1153_bard_analogical_reasoning_affordance
dataset_info:
config_name: plain_text
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 1614
- name: valid
num_examples: 202
- name: test
num_examples: 202
---
# Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1153_bard_analogical_reasoning_affordance
## Dataset Description
- **Homepage:** https://github.com/allenai/natural-instructions
- **Paper:** https://arxiv.org/abs/2204.07705
- **Paper:** https://arxiv.org/abs/2407.00066
- **Point of Contact:** [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
## Additional Information
### Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
```bibtex
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
```
More details can also be found in the following paper:
```bibtex
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
```
### Contact Information
For any comments or questions, please email [Rickard Brüel Gabrielsson](mailto:brg@mit.edu)
|
dgambettavuw/D_gen1_run2_llama2-7b_xlsum_doc1000_real96_synt32_vuw | dgambettavuw | "2025-01-03T19:30:46Z" | 32 | 0 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-01-03T19:30:42Z" | ---
dataset_info:
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configs:
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data_files:
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path: data/train-*
---
|
DT4LM/naive_gpt2_mr_pair_kuleshov_original | DT4LM | "2025-01-03T19:33:23Z" | 32 | 0 | [
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] | null | "2025-01-03T19:33:20Z" | ---
dataset_info:
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configs:
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data_files:
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path: data/train-*
---
|
spiralworks/openreview-structured-reviews-2025 | spiralworks | "2025-01-03T19:47:43Z" | 32 | 0 | [
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"library:polars",
"region:us"
] | null | "2025-01-03T19:45:50Z" | ---
dataset_info:
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configs:
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data_files:
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path: data/train-*
---
|
bilisimhocasi/deepseek | bilisimhocasi | "2025-01-03T20:13:04Z" | 32 | 0 | [
"size_categories:n<1K",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-01-03T20:12:36Z" | ---
dataset_info:
features:
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dtype: string
- name: input
dtype: string
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dtype: string
splits:
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num_bytes: 3656.7
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num_bytes: 394
num_examples: 1
download_size: 10773
dataset_size: 4050.7
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
andabi/D2 | andabi | "2025-01-03T20:29:23Z" | 32 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] | [
"robotics"
] | "2025-01-03T20:29:03Z" | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
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},
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"features": {
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12
],
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"left_elbow_flex",
"left_wrist_flex",
"left_wrist_roll",
"left_gripper",
"right_shoulder_pan",
"right_shoulder_lift",
"right_elbow_flex",
"right_wrist_flex",
"right_wrist_roll",
"right_gripper"
]
},
"observation.state": {
"dtype": "float32",
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12
],
"names": [
"left_shoulder_pan",
"left_shoulder_lift",
"left_elbow_flex",
"left_wrist_flex",
"left_wrist_roll",
"left_gripper",
"right_shoulder_pan",
"right_shoulder_lift",
"right_elbow_flex",
"right_wrist_flex",
"right_wrist_roll",
"right_gripper"
]
},
"observation.images.bird": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 15.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
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},
"observation.images.wrist_left": {
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480,
640,
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],
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"width",
"channels"
],
"info": {
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"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
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}
},
"observation.images.wrist_right": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
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"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"timestamp": {
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1
],
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},
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1
],
"names": null
},
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1
],
"names": null
},
"index": {
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"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
DT4LM/naive_gpt2_rte_pair_kuleshov_original | DT4LM | "2025-01-03T21:10:07Z" | 32 | 0 | [
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"library:pandas",
"library:mlcroissant",
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] | null | "2025-01-03T21:10:04Z" | ---
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---
|
mytestdpo/llama3_gsm8k1_w2c74.5K_c175K | mytestdpo | "2025-01-03T22:36:12Z" | 32 | 0 | [
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] | null | "2025-01-03T22:36:03Z" | ---
dataset_info:
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path: data/train-*
---
|
jkazdan/Mistral-7B-Instruct-v0.2-refusal-5000-HeX-PHI | jkazdan | "2025-01-03T22:36:37Z" | 32 | 0 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
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] | null | "2025-01-03T22:36:36Z" | ---
dataset_info:
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---
|
jkazdan/Mistral-7B-Instruct-v0.2-refusal-5000-AMD | jkazdan | "2025-01-03T22:48:18Z" | 32 | 0 | [
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---
|
selfcorrexp2/w2r100k_r2r40k_r100k | selfcorrexp2 | "2025-01-04T00:07:17Z" | 32 | 0 | [
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] | null | "2025-01-04T00:06:24Z" | ---
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configs:
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data_files:
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---
|
jkazdan/Mistral-7B-Instruct-v0.2-AOA-5000-AOA-5000-hard-no | jkazdan | "2025-01-04T01:56:23Z" | 32 | 0 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-01-04T00:55:45Z" | ---
dataset_info:
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dtype: string
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num_bytes: 177291
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download_size: 96529
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configs:
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data_files:
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path: data/train-*
---
|
DT4LM/naive_albertbasev2_sst2_pair_kuleshov | DT4LM | "2025-01-04T08:37:48Z" | 32 | 0 | [
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] | null | "2025-01-04T03:46:08Z" | ---
dataset_info:
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---
|
DT4LM/t5v1-1base_rte_pair_faster-alzantot_original_4_2 | DT4LM | "2025-01-04T03:57:51Z" | 32 | 0 | [
"size_categories:n<1K",
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"modality:text",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-01-04T03:57:48Z" | ---
dataset_info:
features:
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dtype: string
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dtype: string
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dtype: int32
splits:
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num_bytes: 39335
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download_size: 33030
dataset_size: 39335
---
# Dataset Card for "t5v1-1base_rte_pair_faster-alzantot_original_4_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DT4LM/naive_t5v1-1base_sst2_pair_kuleshov_original | DT4LM | "2025-01-04T03:59:41Z" | 32 | 0 | [
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Hazelnut27/labeled_images_demo | Hazelnut27 | "2025-01-04T05:04:44Z" | 32 | 0 | [
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ZhangShenao/math_metamath-gemma-1.1-7b-it-iter1_sample_1000_tp | ZhangShenao | "2025-01-04T05:35:40Z" | 32 | 0 | [
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|
dgambettavuw/D_gen2_run2_llama2-7b_xlsum_doc1000_real32_synt96_vuw | dgambettavuw | "2025-01-04T06:02:17Z" | 32 | 0 | [
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jpalgo/lj-tts-text-tags-v1 | jpalgo | "2025-01-04T06:18:04Z" | 32 | 0 | [
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|
ZhangShenao/sft-math_metamath-gemma-1.1-7b-it-iter_sample_1000_tp | ZhangShenao | "2025-01-04T07:28:02Z" | 32 | 0 | [
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|
DT4LM/naive_t5v1-1base_sst2_pair_faster-alzantot | DT4LM | "2025-01-08T09:31:02Z" | 32 | 0 | [
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DT4LM/naive_t5v1-1base_sst2_pair_faster-alzantot_original | DT4LM | "2025-01-08T09:31:06Z" | 32 | 0 | [
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|
DT4LM/t5v1-1ba_sst2_leap_differential_original_2_2 | DT4LM | "2025-01-05T12:49:02Z" | 32 | 0 | [
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# Dataset Card for "t5v1-1ba_sst2_leap_differential_original_2_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DT4LM/t5v1-1base_sst2_leap_2_3 | DT4LM | "2025-01-04T14:04:41Z" | 32 | 0 | [
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---
# Dataset Card for "t5v1-1base_sst2_leap_2_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mpdg123456789/quotes-300k-train | mpdg123456789 | "2025-01-04T18:46:39Z" | 32 | 0 | [
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jeffreygwang/step98tester | jeffreygwang | "2025-01-04T20:31:11Z" | 32 | 0 | [
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Renyi444/AdvSpeech | Renyi444 | "2025-01-04T22:30:08Z" | 32 | 0 | [
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|
violetxi/MATH-500_L4_best_first_N128_B4_D15_T0.0001_120-128 | violetxi | "2025-01-05T01:23:53Z" | 32 | 0 | [
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RyanYr/reflect_gsm8k-test_nonGenCritic_t4_binlabel | RyanYr | "2025-01-26T02:28:54Z" | 32 | 0 | [
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Will258/guanaco-llama2-1k | Will258 | "2025-01-05T01:48:53Z" | 32 | 0 | [
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oosless/guanaco-llama2-1k | oosless | "2025-01-05T02:28:59Z" | 32 | 0 | [
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yobro4619/helpful_rm | yobro4619 | "2025-01-05T03:31:55Z" | 32 | 0 | [
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oda-99/test_aozora_head_3_consonant_hira | oda-99 | "2025-01-05T05:08:37Z" | 32 | 0 | [
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oda-99/test_aozora_tail_3_consonant_hira | oda-99 | "2025-01-05T05:09:01Z" | 32 | 0 | [
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oda-99/test_aozora_tail_3_vowel_romaji | oda-99 | "2025-01-05T05:09:17Z" | 32 | 0 | [
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weqweasdas/HanningZhang_Llama3-w2r100k-r2r40k-r100k-3ep_tmp07 | weqweasdas | "2025-01-05T06:25:34Z" | 32 | 0 | [
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|
ajay-pundir/e-cars-analysis | ajay-pundir | "2025-01-05T11:49:24Z" | 32 | 0 | [
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license: mit
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--- |
namejun12000/PALR_inference1_sports | namejun12000 | "2025-01-05T06:49:56Z" | 32 | 0 | [
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- config_name: default
data_files:
- split: train_50_first
path: data/train_50_first-*
- split: train_50_second
path: data/train_50_second-*
---
|
jkazdan/llama-8b-Instruct-refusal-with-system | jkazdan | "2025-01-05T07:08:10Z" | 32 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-01-05T07:08:09Z" | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 157411
num_examples: 300
download_size: 76322
dataset_size: 157411
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TianxingChen/VLA | TianxingChen | "2025-01-05T07:11:09Z" | 32 | 0 | [
"license:mit",
"region:us"
] | null | "2025-01-05T07:11:09Z" | ---
license: mit
---
|
DT4LM/naive_t5v1-1base_rte_pair_leap_original | DT4LM | "2025-01-06T12:54:25Z" | 32 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2025-01-05T09:48:36Z" | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int32
splits:
- name: train
num_bytes: 53206
num_examples: 163
download_size: 41483
dataset_size: 53206
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Subsets and Splits