| | --- |
| | license: apache-2.0 |
| | datasets: |
| | - Jsevisal/gesture_pred |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | widget: |
| | - text: I'm fine. Who is this? |
| | - text: You can't take anything seriously. |
| | - text: In the end he''s going to croak, isn''t he? |
| | pipeline_tag: token-classification |
| | base_model: elastic/distilbert-base-cased-finetuned-conll03-english |
| | model-index: |
| | - name: distilbert-gest-pred-seqeval-partialmatch |
| | 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. --> |
| |
|
| | # distilbert-gest-pred-seqeval-partialmatch |
| |
|
| | This model is a fine-tuned version of [elastic/distilbert-base-cased-finetuned-conll03-english](https://huggingface.co/elastic/distilbert-base-cased-finetuned-conll03-english) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.7300 |
| | - Precision: 0.8116 |
| | - Recall: 0.6988 |
| | - F1: 0.7337 |
| | - Accuracy: 0.8082 |
| |
|
| | ## 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: 2e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 16 |
| | - 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 | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 1.8684 | 1.0 | 147 | 1.1962 | 0.3713 | 0.4095 | 0.3845 | 0.7100 | |
| | | 0.9616 | 2.0 | 294 | 0.8900 | 0.6151 | 0.5556 | 0.5459 | 0.7594 | |
| | | 0.696 | 3.0 | 441 | 0.7715 | 0.5896 | 0.5636 | 0.5634 | 0.7848 | |
| | | 0.5283 | 4.0 | 588 | 0.7300 | 0.8116 | 0.6988 | 0.7337 | 0.8082 | |
| | | 0.4079 | 5.0 | 735 | 0.7423 | 0.7973 | 0.6971 | 0.7258 | 0.8134 | |
| | | 0.309 | 6.0 | 882 | 0.8589 | 0.8034 | 0.6935 | 0.7185 | 0.7965 | |
| | | 0.2629 | 7.0 | 1029 | 0.8160 | 0.8076 | 0.6955 | 0.7268 | 0.7958 | |
| | | 0.2059 | 8.0 | 1176 | 0.8178 | 0.8116 | 0.7130 | 0.7382 | 0.8127 | |
| | | 0.1701 | 9.0 | 1323 | 0.8471 | 0.7981 | 0.7214 | 0.7365 | 0.8101 | |
| | | 0.1574 | 10.0 | 1470 | 0.8515 | 0.7956 | 0.7216 | 0.7363 | 0.8088 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.27.2 |
| | - Pytorch 1.13.1+cu116 |
| | - Datasets 2.10.1 |
| | - Tokenizers 0.13.2 |
