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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- indian_names |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: my_awesome_wnut_model |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: indian_names |
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type: indian_names |
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config: indian_names |
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split: train |
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args: indian_names |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9941348973607038 |
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- name: Recall |
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type: recall |
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value: 0.9921951219512195 |
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- name: F1 |
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type: f1 |
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value: 0.9931640625 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9998052125131481 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# my_awesome_wnut_model |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the indian_names dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0012 |
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- Precision: 0.9941 |
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- Recall: 0.9922 |
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- F1: 0.9932 |
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- Accuracy: 0.9998 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 63 | 0.1413 | 0.0 | 0.0 | 0.0 | 0.9745 | |
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| No log | 2.0 | 126 | 0.1211 | 0.0 | 0.0 | 0.0 | 0.9745 | |
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| No log | 3.0 | 189 | 0.0656 | 0.6231 | 0.2790 | 0.3854 | 0.9811 | |
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| No log | 4.0 | 252 | 0.0380 | 0.7297 | 0.6059 | 0.6620 | 0.9894 | |
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| No log | 5.0 | 315 | 0.0259 | 0.8341 | 0.7259 | 0.7762 | 0.9931 | |
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| No log | 6.0 | 378 | 0.0136 | 0.8842 | 0.8712 | 0.8776 | 0.9963 | |
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| No log | 7.0 | 441 | 0.0076 | 0.9286 | 0.9268 | 0.9277 | 0.9981 | |
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| 0.0748 | 8.0 | 504 | 0.0054 | 0.9409 | 0.9473 | 0.9441 | 0.9985 | |
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| 0.0748 | 9.0 | 567 | 0.0042 | 0.9520 | 0.9678 | 0.9598 | 0.9991 | |
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| 0.0748 | 10.0 | 630 | 0.0025 | 0.9738 | 0.9795 | 0.9767 | 0.9995 | |
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| 0.0748 | 11.0 | 693 | 0.0019 | 0.9863 | 0.9863 | 0.9863 | 0.9997 | |
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| 0.0748 | 12.0 | 756 | 0.0015 | 0.9961 | 0.9912 | 0.9936 | 0.9998 | |
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| 0.0748 | 13.0 | 819 | 0.0014 | 0.9912 | 0.9912 | 0.9912 | 0.9998 | |
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| 0.0748 | 14.0 | 882 | 0.0013 | 0.9912 | 0.9912 | 0.9912 | 0.9998 | |
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| 0.0748 | 15.0 | 945 | 0.0012 | 0.9941 | 0.9922 | 0.9932 | 0.9998 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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