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--- |
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language: |
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- en |
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license: mit |
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base_model: prajjwal1/bert-tiny |
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tags: |
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- pytorch |
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- BertForTokenClassification |
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- named-entity-recognition |
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- roberta-base |
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- generated_from_trainer |
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metrics: |
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- recall |
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- precision |
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- f1 |
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- accuracy |
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model-index: |
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- name: bert-tiny-ontonotes |
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results: [] |
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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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# bert-tiny-ontonotes |
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This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the tner/ontonotes5 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1917 |
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- Recall: 0.7193 |
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- Precision: 0.6817 |
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- F1: 0.7000 |
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- Accuracy: 0.9476 |
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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: 8e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 160 |
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- seed: 75241309 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- training_steps: 6000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Recall | Precision | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| |
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| 0.4283 | 0.31 | 600 | 0.3864 | 0.4561 | 0.4260 | 0.4405 | 0.9058 | |
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| 0.3214 | 0.63 | 1200 | 0.2865 | 0.5865 | 0.5485 | 0.5669 | 0.9265 | |
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| 0.2886 | 0.94 | 1800 | 0.2439 | 0.6432 | 0.6165 | 0.6295 | 0.9354 | |
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| 0.2511 | 1.25 | 2400 | 0.2233 | 0.6765 | 0.6250 | 0.6497 | 0.9389 | |
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| 0.2224 | 1.56 | 3000 | 0.2088 | 0.6878 | 0.6642 | 0.6758 | 0.9433 | |
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| 0.2181 | 1.88 | 3600 | 0.2001 | 0.7105 | 0.6684 | 0.6888 | 0.9451 | |
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| 0.215 | 2.19 | 4200 | 0.1954 | 0.7140 | 0.6795 | 0.6963 | 0.9469 | |
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| 0.1907 | 2.5 | 4800 | 0.1934 | 0.7169 | 0.6776 | 0.6967 | 0.9470 | |
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| 0.209 | 2.82 | 5400 | 0.1918 | 0.7185 | 0.6812 | 0.6994 | 0.9475 | |
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| 0.2073 | 3.13 | 6000 | 0.1917 | 0.7193 | 0.6817 | 0.7000 | 0.9476 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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