update model card README.md
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README.md
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- generated_from_trainer
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datasets:
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- wnut_17
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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: token_classification_wnut
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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: wnut_17
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type: wnut_17
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config: wnut_17
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split: train
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args: wnut_17
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metrics:
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- name: Precision
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type: precision
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value: 0.5846994535519126
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- name: Recall
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type: recall
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value: 0.39666357738646896
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- name: F1
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type: f1
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value: 0.47266703478741023
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- name: Accuracy
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type: accuracy
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value: 0.947714933093925
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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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# token_classification_wnut
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This model is a fine-tuned version of [
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It achieves the following results on the evaluation set:
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- Loss: 0.2932
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- Precision: 0.5847
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- Recall: 0.3967
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- F1: 0.4727
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- Accuracy: 0.9477
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate:
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- train_batch_size:
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- eval_batch_size:
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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:
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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 |
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| No log | 2.0 | 214 | 0.2932 | 0.5847 | 0.3967 | 0.4727 | 0.9477 |
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### Framework versions
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- generated_from_trainer
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datasets:
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- wnut_17
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model-index:
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- name: token_classification_wnut
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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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# token_classification_wnut
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This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the wnut_17 dataset.
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 4e-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: 1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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| No log | 1.0 | 213 | 0.3717 | 0.6279 | 0.3707 | 0.4662 | 0.9481 |
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### Framework versions
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