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BERT-full-finetuned-ner-pablo

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  2. runs/Sep05_11-30-42_24b5dd7b9e65/events.out.tfevents.1725535843.24b5dd7b9e65.183.0 +2 -2
README.md CHANGED
@@ -21,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1111
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- - Precision: 0.8022
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- - Recall: 0.7972
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- - F1: 0.7997
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- - Accuracy: 0.9747
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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: 0.0002
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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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- - gradient_accumulation_steps: 4
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- - total_train_batch_size: 64
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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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- - lr_scheduler_warmup_ratio: 0.05
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- - num_epochs: 5
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  - mixed_precision_training: Native AMP
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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 | 0.9970 | 252 | 0.0973 | 0.7590 | 0.7702 | 0.7646 | 0.9726 |
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- | 0.164 | 1.9980 | 505 | 0.0867 | 0.7999 | 0.7776 | 0.7886 | 0.9751 |
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- | 0.164 | 2.9990 | 758 | 0.0903 | 0.8044 | 0.7862 | 0.7952 | 0.9747 |
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- | 0.0439 | 4.0 | 1011 | 0.0970 | 0.8032 | 0.7960 | 0.7996 | 0.9746 |
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- | 0.0439 | 4.9852 | 1260 | 0.1111 | 0.8022 | 0.7972 | 0.7997 | 0.9747 |
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  ### Framework versions
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  - Transformers 4.44.2
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- - Pytorch 2.4.0+cu121
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  - Datasets 2.21.0
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  - Tokenizers 0.19.1
 
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  This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0717
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+ - Precision: 0.8057
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+ - Recall: 0.8122
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+ - F1: 0.8090
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+ - Accuracy: 0.9784
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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: 5e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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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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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 2
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  - mixed_precision_training: Native AMP
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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 | 231 | 0.0979 | 0.7350 | 0.7877 | 0.7604 | 0.9708 |
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+ | No log | 2.0 | 462 | 0.0717 | 0.8057 | 0.8122 | 0.8090 | 0.9784 |
 
 
 
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  ### Framework versions
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  - Transformers 4.44.2
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+ - Pytorch 2.4.1+cu121
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  - Datasets 2.21.0
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  - Tokenizers 0.19.1
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