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# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 | |
# Doc / guide: https://huggingface.co/docs/hub/model-cards | |
{{ card_data }} | |
# {{ model_name if model_name else ( "SetFit Aspect Model for Aspect Based Sentiment Analysis" if is_aspect else ( "SetFit Polarity Model for Aspect Based Sentiment Analysis" if is_aspect is False else "SetFit Model for Text Classification"))}} | |
This is a [SetFit](https://github.com/huggingface/setfit) model{% if dataset_id %} trained on the [{{ dataset_name if dataset_name else dataset_id }}](https://huggingface.co/datasets/{{ dataset_id }}) dataset{% endif %} that can be used for {{ task_name | default("Text Classification", true) }}.{% if st_id %} This SetFit model uses [{{ st_id }}](https://huggingface.co/{{ st_id }}) as the Sentence Transformer embedding model.{% endif %} A {{ head_class }} instance is used for classification.{% if is_absa %} In particular, this model is in charge of {{ "filtering aspect span candidates" if is_aspect else "classifying aspect polarities"}}.{% endif %} | |
The model has been trained using an efficient few-shot learning technique that involves: | |
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. | |
2. Training a classification head with features from the fine-tuned Sentence Transformer. | |
{% if is_absa %} | |
This model was trained within the context of a larger system for ABSA, which looks like so: | |
1. Use a spaCy model to select possible aspect span candidates. | |
2. {{ "**" if is_aspect else "" }}Use {{ "this" if is_aspect else "a" }} SetFit model to filter these possible aspect span candidates.{{ "**" if is_aspect else "" }} | |
3. {{ "**" if not is_aspect else "" }}Use {{ "this" if not is_aspect else "a" }} SetFit model to classify the filtered aspect span candidates.{{ "**" if not is_aspect else "" }} | |
{% endif %} | |
## Model Details | |
### Model Description | |
- **Model Type:** SetFit | |
{% if st_id -%} | |
- **Sentence Transformer body:** [{{ st_id }}](https://huggingface.co/{{ st_id }}) | |
{%- else -%} | |
<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) --> | |
{%- endif %} | |
{% if head_class -%} | |
- **Classification head:** a {{ head_class }} instance | |
{%- else -%} | |
<!-- - **Classification head:** Unknown --> | |
{%- endif %} | |
{%- if spacy_model %} | |
- **spaCy Model:** {{ spacy_model }} | |
{%- endif %} | |
{%- if aspect_model %} | |
- **SetFitABSA Aspect Model:** [{{ aspect_model }}](https://huggingface.co/{{ aspect_model }}) | |
{%- endif %} | |
{%- if polarity_model %} | |
- **SetFitABSA Polarity Model:** [{{ polarity_model }}](https://huggingface.co/{{ polarity_model }}) | |
{%- endif %} | |
- **Maximum Sequence Length:** {{ model_max_length }} tokens | |
{% if num_classes -%} | |
- **Number of Classes:** {{ num_classes }} classes | |
{%- else -%} | |
<!-- - **Number of Classes:** Unknown --> | |
{%- endif %} | |
{% if dataset_id -%} | |
- **Training Dataset:** [{{ dataset_name if dataset_name else dataset_id }}](https://huggingface.co/datasets/{{ dataset_id }}) | |
{%- else -%} | |
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> | |
{%- endif %} | |
{% if language -%} | |
- **Language{{"s" if language is not string and language | length > 1 else ""}}:** | |
{%- if language is string %} {{ language }} | |
{%- else %} {% for lang in language -%} | |
{{ lang }}{{ ", " if not loop.last else "" }} | |
{%- endfor %} | |
{%- endif %} | |
{%- else -%} | |
<!-- - **Language:** Unknown --> | |
{%- endif %} | |
{% if license -%} | |
- **License:** {{ license }} | |
{%- else -%} | |
<!-- - **License:** Unknown --> | |
{%- endif %} | |
### Model Sources | |
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) | |
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) | |
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) | |
{% if label_examples %} | |
### Model Labels | |
{{ label_examples }}{% endif -%} | |
{% if metrics_table %} | |
## Evaluation | |
### Metrics | |
{{ metrics_table }}{% endif %} | |
## Uses | |
### Direct Use for Inference | |
First install the SetFit library: | |
```bash | |
pip install setfit | |
``` | |
Then you can load this model and run inference. | |
{% if is_absa %} | |
```python | |
from setfit import AbsaModel | |
# Download from the {{ hf_emoji }} Hub | |
model = AbsaModel.from_pretrained( | |
"{{ aspect_model }}", | |
"{{ polarity_model }}", | |
) | |
# Run inference | |
preds = model("The food was great, but the venue is just way too busy.") | |
``` | |
{%- else %} | |
```python | |
from setfit import SetFitModel | |
# Download from the {{ hf_emoji }} Hub | |
model = SetFitModel.from_pretrained("{{ model_id | default('setfit_model_id', true) }}") | |
# Run inference | |
preds = model("{{ predict_example | default("I loved the spiderman movie!", true) | replace('"', '\\"') }}") | |
``` | |
{%- endif %} | |
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### Downstream Use | |
*List how someone could finetune this model on their own dataset.* | |
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### Out-of-Scope Use | |
*List how the model may foreseeably be misused and address what users ought not to do with the model.* | |
--> | |
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## Bias, Risks and Limitations | |
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* | |
--> | |
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### Recommendations | |
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* | |
--> | |
## Training Details | |
{% if train_set_metrics %} | |
### Training Set Metrics | |
{{ train_set_metrics }}{% if train_set_sentences_per_label_list %} | |
{{ train_set_sentences_per_label_list }}{% endif %}{% endif %}{% if hyperparameters %} | |
### Training Hyperparameters | |
{% for name, value in hyperparameters.items() %}- {{ name }}: {{ value }} | |
{% endfor %}{% endif %}{% if eval_lines %} | |
### Training Results | |
{{ eval_lines }}{% if explain_bold_in_eval %} | |
* The bold row denotes the saved checkpoint.{% endif %}{% endif %}{% if co2_eq_emissions %} | |
### Environmental Impact | |
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). | |
- **Carbon Emitted**: {{ "%.3f"|format(co2_eq_emissions["emissions"] / 1000) }} kg of CO2 | |
- **Hours Used**: {{ co2_eq_emissions["hours_used"] }} hours | |
### Training Hardware | |
- **On Cloud**: {{ "Yes" if co2_eq_emissions["on_cloud"] else "No" }} | |
- **GPU Model**: {{ co2_eq_emissions["hardware_used"] or "No GPU used" }} | |
- **CPU Model**: {{ co2_eq_emissions["cpu_model"] }} | |
- **RAM Size**: {{ "%.2f"|format(co2_eq_emissions["ram_total_size"]) }} GB | |
{% endif %} | |
### Framework Versions | |
- Python: {{ version["python"] }} | |
- SetFit: {{ version["setfit"] }} | |
- Sentence Transformers: {{ version["sentence_transformers"] }} | |
{%- if "spacy" in version %} | |
- spaCy: {{ version["spacy"] }} | |
{%- endif %} | |
- Transformers: {{ version["transformers"] }} | |
- PyTorch: {{ version["torch"] }} | |
- Datasets: {{ version["datasets"] }} | |
- Tokenizers: {{ version["tokenizers"] }} | |
## Citation | |
### BibTeX | |
```bibtex | |
@article{https://doi.org/10.48550/arxiv.2209.11055, | |
doi = {10.48550/ARXIV.2209.11055}, | |
url = {https://arxiv.org/abs/2209.11055}, | |
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, | |
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, | |
title = {Efficient Few-Shot Learning Without Prompts}, | |
publisher = {arXiv}, | |
year = {2022}, | |
copyright = {Creative Commons Attribution 4.0 International} | |
} | |
``` | |
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## Glossary | |
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