Datasets:

librarian-bots

/

model_cards_with_metadata

like 10

dogssss/Qwen-Qwen1.5-1.8B-1727227989

dogssss

peft

--- base_model: Qwen/Qwen1.5-1.8B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [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 (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, 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 model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.12.0

Dhurkesh1/tomatoDiseaseClassifier

Dhurkesh1

image-classification

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: tomatoDiseaseClassifier results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9908371567726135 --- # tomatoDiseaseClassifier Autogenerated by HuggingPics🤗🖼️ This model is designed for classifying [YOUR TASK] images. It was fine-tuned using PyTorch Lightning and Hugging Face transformers. ## Example Images #### Tomato_Bacterial_spot  #### Tomato_Early_blight  #### Tomato_Late_blight  #### Tomato_Leaf_Mold  #### Tomato_Septoria_leaf_spot  #### Tomato_Spider_mites_Two_spotted_spider_mite  #### Tomato__Target_Spot  #### Tomato__Tomato_YellowLeaf__Curl_Virus  #### Tomato__Tomato_mosaic_virus  #### Tomato_healthy 

agamgoy/lora-task-1-group-Entertainment

agamgoy

transformers

--- base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** agamgoy - **License:** apache-2.0 - **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

SALUTEASD/Qwen-Qwen1.5-0.5B-1727227998

SALUTEASD

peft

--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [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 (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, 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 model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.12.0

xueyj/Qwen-Qwen1.5-0.5B-1727228010

xueyj

peft

--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [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 (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, 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 model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.12.0

Kudod/roberta-large-ner-ghtk-cs-add-6label-10-new-data-3090-25Sep-1

Kudod

--- tags: - generated_from_trainer model-index: - name: roberta-large-ner-ghtk-cs-add-6label-10-new-data-3090-25Sep-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-ner-ghtk-cs-add-6label-10-new-data-3090-25Sep-1 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3247 - Tk: {'precision': 0.6533333333333333, 'recall': 0.4224137931034483, 'f1': 0.513089005235602, 'number': 116} - A: {'precision': 0.9223946784922394, 'recall': 0.9651972157772621, 'f1': 0.9433106575963719, 'number': 431} - Gày: {'precision': 0.7272727272727273, 'recall': 0.9411764705882353, 'f1': 0.8205128205128205, 'number': 34} - Gày trừu tượng: {'precision': 0.9056603773584906, 'recall': 0.8852459016393442, 'f1': 0.8953367875647669, 'number': 488} - Gân hàng: {'precision': 0.8717948717948718, 'recall': 0.918918918918919, 'f1': 0.8947368421052632, 'number': 37} - Iền: {'precision': 0.7115384615384616, 'recall': 0.9487179487179487, 'f1': 0.8131868131868132, 'number': 39} - Iờ: {'precision': 0.6458333333333334, 'recall': 0.8157894736842105, 'f1': 0.7209302325581395, 'number': 38} - Ã đơn: {'precision': 0.855, 'recall': 0.8423645320197044, 'f1': 0.8486352357320099, 'number': 203} - Đt: {'precision': 0.9220917822838848, 'recall': 0.9840546697038725, 'f1': 0.9520661157024795, 'number': 878} - Đt trừu tượng: {'precision': 0.7786259541984732, 'recall': 0.8755364806866953, 'f1': 0.8242424242424242, 'number': 233} - Ịa chỉ cụ thể: {'precision': 0.4375, 'recall': 0.4883720930232558, 'f1': 0.4615384615384615, 'number': 43} - Ịa chỉ trừu tượng: {'precision': 0.6933333333333334, 'recall': 0.6842105263157895, 'f1': 0.6887417218543047, 'number': 76} - Overall Precision: 0.8652 - Overall Recall: 0.8956 - Overall F1: 0.8802 - Overall Accuracy: 0.9466 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Tk | A | Gày | Gày trừu tượng | Gân hàng | Iền | Iờ | Ã đơn | Đt | Đt trừu tượng | Ịa chỉ cụ thể | Ịa chỉ trừu tượng | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | No log | 1.0 | 367 | 0.1996 | {'precision': 0.4666666666666667, 'recall': 0.2413793103448276, 'f1': 0.3181818181818182, 'number': 116} | {'precision': 0.9269911504424779, 'recall': 0.9721577726218097, 'f1': 0.9490373725934316, 'number': 431} | {'precision': 0.7435897435897436, 'recall': 0.8529411764705882, 'f1': 0.7945205479452054, 'number': 34} | {'precision': 0.890495867768595, 'recall': 0.8831967213114754, 'f1': 0.8868312757201646, 'number': 488} | {'precision': 0.8787878787878788, 'recall': 0.7837837837837838, 'f1': 0.8285714285714285, 'number': 37} | {'precision': 0.7560975609756098, 'recall': 0.7948717948717948, 'f1': 0.7749999999999999, 'number': 39} | {'precision': 0.5961538461538461, 'recall': 0.8157894736842105, 'f1': 0.6888888888888889, 'number': 38} | {'precision': 0.6548042704626335, 'recall': 0.9064039408866995, 'f1': 0.7603305785123967, 'number': 203} | {'precision': 0.9236479321314952, 'recall': 0.9920273348519362, 'f1': 0.956617243272927, 'number': 878} | {'precision': 0.753731343283582, 'recall': 0.8669527896995708, 'f1': 0.8063872255489022, 'number': 233} | {'precision': 0.2727272727272727, 'recall': 0.3488372093023256, 'f1': 0.30612244897959184, 'number': 43} | {'precision': 0.7678571428571429, 'recall': 0.5657894736842105, 'f1': 0.6515151515151516, 'number': 76} | 0.8368 | 0.8842 | 0.8599 | 0.9315 | | 0.2043 | 2.0 | 734 | 0.2190 | {'precision': 0.5594405594405595, 'recall': 0.6896551724137931, 'f1': 0.6177606177606177, 'number': 116} | {'precision': 0.9027484143763214, 'recall': 0.9907192575406032, 'f1': 0.9446902654867256, 'number': 431} | {'precision': 0.7631578947368421, 'recall': 0.8529411764705882, 'f1': 0.8055555555555555, 'number': 34} | {'precision': 0.8893360160965795, 'recall': 0.9057377049180327, 'f1': 0.8974619289340101, 'number': 488} | {'precision': 0.8571428571428571, 'recall': 0.8108108108108109, 'f1': 0.8333333333333334, 'number': 37} | {'precision': 0.7083333333333334, 'recall': 0.8717948717948718, 'f1': 0.7816091954022988, 'number': 39} | {'precision': 0.5714285714285714, 'recall': 0.9473684210526315, 'f1': 0.7128712871287128, 'number': 38} | {'precision': 0.7355371900826446, 'recall': 0.8768472906403941, 'f1': 0.7999999999999999, 'number': 203} | {'precision': 0.9700115340253749, 'recall': 0.9578587699316629, 'f1': 0.9638968481375358, 'number': 878} | {'precision': 0.8292682926829268, 'recall': 0.8755364806866953, 'f1': 0.8517745302713987, 'number': 233} | {'precision': 0.2714285714285714, 'recall': 0.4418604651162791, 'f1': 0.33628318584070793, 'number': 43} | {'precision': 0.7936507936507936, 'recall': 0.6578947368421053, 'f1': 0.7194244604316548, 'number': 76} | 0.8510 | 0.9060 | 0.8776 | 0.9404 | | 0.1101 | 3.0 | 1101 | 0.2292 | {'precision': 0.608, 'recall': 0.6551724137931034, 'f1': 0.6307053941908715, 'number': 116} | {'precision': 0.9038461538461539, 'recall': 0.9814385150812065, 'f1': 0.9410456062291435, 'number': 431} | {'precision': 0.7209302325581395, 'recall': 0.9117647058823529, 'f1': 0.8051948051948051, 'number': 34} | {'precision': 0.9119496855345912, 'recall': 0.8913934426229508, 'f1': 0.9015544041450777, 'number': 488} | {'precision': 0.825, 'recall': 0.8918918918918919, 'f1': 0.8571428571428571, 'number': 37} | {'precision': 0.75, 'recall': 0.9230769230769231, 'f1': 0.8275862068965517, 'number': 39} | {'precision': 0.5147058823529411, 'recall': 0.9210526315789473, 'f1': 0.660377358490566, 'number': 38} | {'precision': 0.7295081967213115, 'recall': 0.8768472906403941, 'f1': 0.796420581655481, 'number': 203} | {'precision': 0.8975409836065574, 'recall': 0.9977220956719818, 'f1': 0.9449838187702265, 'number': 878} | {'precision': 0.8064516129032258, 'recall': 0.8583690987124464, 'f1': 0.8316008316008315, 'number': 233} | {'precision': 0.3088235294117647, 'recall': 0.4883720930232558, 'f1': 0.3783783783783784, 'number': 43} | {'precision': 0.6865671641791045, 'recall': 0.6052631578947368, 'f1': 0.6433566433566432, 'number': 76} | 0.8322 | 0.9136 | 0.8710 | 0.9398 | | 0.1101 | 4.0 | 1468 | 0.2232 | {'precision': 0.5283018867924528, 'recall': 0.4827586206896552, 'f1': 0.5045045045045045, 'number': 116} | {'precision': 0.9440559440559441, 'recall': 0.9396751740139211, 'f1': 0.9418604651162792, 'number': 431} | {'precision': 0.717391304347826, 'recall': 0.9705882352941176, 'f1': 0.825, 'number': 34} | {'precision': 0.9216101694915254, 'recall': 0.8913934426229508, 'f1': 0.90625, 'number': 488} | {'precision': 0.8048780487804879, 'recall': 0.8918918918918919, 'f1': 0.8461538461538461, 'number': 37} | {'precision': 0.6551724137931034, 'recall': 0.9743589743589743, 'f1': 0.7835051546391754, 'number': 39} | {'precision': 0.696969696969697, 'recall': 0.6052631578947368, 'f1': 0.6478873239436619, 'number': 38} | {'precision': 0.8, 'recall': 0.8472906403940886, 'f1': 0.8229665071770335, 'number': 203} | {'precision': 0.9160545645330536, 'recall': 0.9943052391799544, 'f1': 0.953577280174768, 'number': 878} | {'precision': 0.7924528301886793, 'recall': 0.9012875536480687, 'f1': 0.8433734939759037, 'number': 233} | {'precision': 0.46, 'recall': 0.5348837209302325, 'f1': 0.4946236559139785, 'number': 43} | {'precision': 0.6071428571428571, 'recall': 0.6710526315789473, 'f1': 0.6374999999999998, 'number': 76} | 0.8547 | 0.8991 | 0.8763 | 0.9413 | | 0.0712 | 5.0 | 1835 | 0.2456 | {'precision': 0.5662650602409639, 'recall': 0.4051724137931034, 'f1': 0.4723618090452261, 'number': 116} | {'precision': 0.9170305676855895, 'recall': 0.974477958236659, 'f1': 0.9448818897637796, 'number': 431} | {'precision': 0.8048780487804879, 'recall': 0.9705882352941176, 'f1': 0.8800000000000001, 'number': 34} | {'precision': 0.8846153846153846, 'recall': 0.8954918032786885, 'f1': 0.890020366598778, 'number': 488} | {'precision': 0.8888888888888888, 'recall': 0.8648648648648649, 'f1': 0.8767123287671232, 'number': 37} | {'precision': 0.72, 'recall': 0.9230769230769231, 'f1': 0.8089887640449438, 'number': 39} | {'precision': 0.8260869565217391, 'recall': 0.5, 'f1': 0.6229508196721311, 'number': 38} | {'precision': 0.8586387434554974, 'recall': 0.8078817733990148, 'f1': 0.83248730964467, 'number': 203} | {'precision': 0.9177350427350427, 'recall': 0.9783599088838268, 'f1': 0.9470782800441013, 'number': 878} | {'precision': 0.8362068965517241, 'recall': 0.8326180257510729, 'f1': 0.8344086021505375, 'number': 233} | {'precision': 0.475, 'recall': 0.4418604651162791, 'f1': 0.4578313253012048, 'number': 43} | {'precision': 0.6842105263157895, 'recall': 0.6842105263157895, 'f1': 0.6842105263157895, 'number': 76} | 0.8692 | 0.8838 | 0.8764 | 0.9412 | | 0.0416 | 6.0 | 2202 | 0.2494 | {'precision': 0.7066666666666667, 'recall': 0.45689655172413796, 'f1': 0.5549738219895288, 'number': 116} | {'precision': 0.9122055674518201, 'recall': 0.988399071925754, 'f1': 0.9487750556792873, 'number': 431} | {'precision': 0.6875, 'recall': 0.9705882352941176, 'f1': 0.8048780487804877, 'number': 34} | {'precision': 0.8888888888888888, 'recall': 0.9016393442622951, 'f1': 0.8952187182095626, 'number': 488} | {'precision': 0.8648648648648649, 'recall': 0.8648648648648649, 'f1': 0.8648648648648649, 'number': 37} | {'precision': 0.7450980392156863, 'recall': 0.9743589743589743, 'f1': 0.8444444444444443, 'number': 39} | {'precision': 0.6206896551724138, 'recall': 0.9473684210526315, 'f1': 0.75, 'number': 38} | {'precision': 0.8254716981132075, 'recall': 0.8620689655172413, 'f1': 0.8433734939759036, 'number': 203} | {'precision': 0.9202586206896551, 'recall': 0.9726651480637813, 'f1': 0.945736434108527, 'number': 878} | {'precision': 0.7918367346938775, 'recall': 0.8326180257510729, 'f1': 0.811715481171548, 'number': 233} | {'precision': 0.4117647058823529, 'recall': 0.4883720930232558, 'f1': 0.44680851063829785, 'number': 43} | {'precision': 0.6125, 'recall': 0.6447368421052632, 'f1': 0.6282051282051283, 'number': 76} | 0.8558 | 0.8987 | 0.8767 | 0.9453 | | 0.0285 | 7.0 | 2569 | 0.2842 | {'precision': 0.6486486486486487, 'recall': 0.41379310344827586, 'f1': 0.5052631578947369, 'number': 116} | {'precision': 0.9227373068432672, 'recall': 0.9698375870069605, 'f1': 0.9457013574660633, 'number': 431} | {'precision': 0.7272727272727273, 'recall': 0.9411764705882353, 'f1': 0.8205128205128205, 'number': 34} | {'precision': 0.882, 'recall': 0.9036885245901639, 'f1': 0.8927125506072875, 'number': 488} | {'precision': 0.8717948717948718, 'recall': 0.918918918918919, 'f1': 0.8947368421052632, 'number': 37} | {'precision': 0.7115384615384616, 'recall': 0.9487179487179487, 'f1': 0.8131868131868132, 'number': 39} | {'precision': 0.6818181818181818, 'recall': 0.7894736842105263, 'f1': 0.7317073170731707, 'number': 38} | {'precision': 0.8624338624338624, 'recall': 0.8029556650246306, 'f1': 0.8316326530612244, 'number': 203} | {'precision': 0.9334787350054525, 'recall': 0.9749430523917996, 'f1': 0.9537604456824513, 'number': 878} | {'precision': 0.7675276752767528, 'recall': 0.8927038626609443, 'f1': 0.8253968253968255, 'number': 233} | {'precision': 0.45098039215686275, 'recall': 0.5348837209302325, 'f1': 0.48936170212765956, 'number': 43} | {'precision': 0.6582278481012658, 'recall': 0.6842105263157895, 'f1': 0.6709677419354839, 'number': 76} | 0.8633 | 0.8953 | 0.8790 | 0.9458 | | 0.0285 | 8.0 | 2936 | 0.3072 | {'precision': 0.6172839506172839, 'recall': 0.43103448275862066, 'f1': 0.5076142131979695, 'number': 116} | {'precision': 0.9185022026431718, 'recall': 0.9675174013921114, 'f1': 0.9423728813559322, 'number': 431} | {'precision': 0.7272727272727273, 'recall': 0.9411764705882353, 'f1': 0.8205128205128205, 'number': 34} | {'precision': 0.8904665314401623, 'recall': 0.8995901639344263, 'f1': 0.8950050968399593, 'number': 488} | {'precision': 0.8717948717948718, 'recall': 0.918918918918919, 'f1': 0.8947368421052632, 'number': 37} | {'precision': 0.6491228070175439, 'recall': 0.9487179487179487, 'f1': 0.7708333333333334, 'number': 39} | {'precision': 0.6956521739130435, 'recall': 0.8421052631578947, 'f1': 0.761904761904762, 'number': 38} | {'precision': 0.8465346534653465, 'recall': 0.8423645320197044, 'f1': 0.8444444444444443, 'number': 203} | {'precision': 0.9174603174603174, 'recall': 0.9874715261958997, 'f1': 0.9511793746571585, 'number': 878} | {'precision': 0.7928286852589641, 'recall': 0.8540772532188842, 'f1': 0.8223140495867769, 'number': 233} | {'precision': 0.39215686274509803, 'recall': 0.46511627906976744, 'f1': 0.425531914893617, 'number': 43} | {'precision': 0.6091954022988506, 'recall': 0.6973684210526315, 'f1': 0.6503067484662576, 'number': 76} | 0.8549 | 0.8987 | 0.8763 | 0.9453 | | 0.0168 | 9.0 | 3303 | 0.3166 | {'precision': 0.6447368421052632, 'recall': 0.4224137931034483, 'f1': 0.5104166666666667, 'number': 116} | {'precision': 0.9222222222222223, 'recall': 0.962877030162413, 'f1': 0.94211123723042, 'number': 431} | {'precision': 0.7111111111111111, 'recall': 0.9411764705882353, 'f1': 0.8101265822784811, 'number': 34} | {'precision': 0.896049896049896, 'recall': 0.8831967213114754, 'f1': 0.8895768833849329, 'number': 488} | {'precision': 0.8717948717948718, 'recall': 0.918918918918919, 'f1': 0.8947368421052632, 'number': 37} | {'precision': 0.6851851851851852, 'recall': 0.9487179487179487, 'f1': 0.7956989247311828, 'number': 39} | {'precision': 0.62, 'recall': 0.8157894736842105, 'f1': 0.7045454545454546, 'number': 38} | {'precision': 0.8373205741626795, 'recall': 0.8620689655172413, 'f1': 0.8495145631067961, 'number': 203} | {'precision': 0.9204665959703076, 'recall': 0.9886104783599089, 'f1': 0.9533223503569467, 'number': 878} | {'precision': 0.7876447876447876, 'recall': 0.8755364806866953, 'f1': 0.8292682926829268, 'number': 233} | {'precision': 0.4583333333333333, 'recall': 0.5116279069767442, 'f1': 0.4835164835164835, 'number': 43} | {'precision': 0.6891891891891891, 'recall': 0.6710526315789473, 'f1': 0.68, 'number': 76} | 0.8611 | 0.8979 | 0.8791 | 0.9454 | | 0.0095 | 10.0 | 3670 | 0.3247 | {'precision': 0.6533333333333333, 'recall': 0.4224137931034483, 'f1': 0.513089005235602, 'number': 116} | {'precision': 0.9223946784922394, 'recall': 0.9651972157772621, 'f1': 0.9433106575963719, 'number': 431} | {'precision': 0.7272727272727273, 'recall': 0.9411764705882353, 'f1': 0.8205128205128205, 'number': 34} | {'precision': 0.9056603773584906, 'recall': 0.8852459016393442, 'f1': 0.8953367875647669, 'number': 488} | {'precision': 0.8717948717948718, 'recall': 0.918918918918919, 'f1': 0.8947368421052632, 'number': 37} | {'precision': 0.7115384615384616, 'recall': 0.9487179487179487, 'f1': 0.8131868131868132, 'number': 39} | {'precision': 0.6458333333333334, 'recall': 0.8157894736842105, 'f1': 0.7209302325581395, 'number': 38} | {'precision': 0.855, 'recall': 0.8423645320197044, 'f1': 0.8486352357320099, 'number': 203} | {'precision': 0.9220917822838848, 'recall': 0.9840546697038725, 'f1': 0.9520661157024795, 'number': 878} | {'precision': 0.7786259541984732, 'recall': 0.8755364806866953, 'f1': 0.8242424242424242, 'number': 233} | {'precision': 0.4375, 'recall': 0.4883720930232558, 'f1': 0.4615384615384615, 'number': 43} | {'precision': 0.6933333333333334, 'recall': 0.6842105263157895, 'f1': 0.6887417218543047, 'number': 76} | 0.8652 | 0.8956 | 0.8802 | 0.9466 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.3.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1