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# checkpoint-1000

This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0543
- Precision: 0.8457
- Recall: 0.8906
- F1: 0.8676
- Accuracy: 0.9851

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 340  | 0.0596          | 0.7778    | 0.875  | 0.8235 | 0.9795   |
| 0.0787        | 2.0   | 680  | 0.0416          | 0.8246    | 0.8865 | 0.8544 | 0.9851   |
| 0.0202        | 3.0   | 1020 | 0.0494          | 0.8385    | 0.8812 | 0.8593 | 0.9846   |
| 0.0202        | 4.0   | 1360 | 0.0543          | 0.8457    | 0.8906 | 0.8676 | 0.9851   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["ncbi_disease"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext", "model-index": [{"name": "checkpoint-1000", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "ncbi_disease", "type": "ncbi_disease", "config": "ncbi_disease", "split": "test", "args": "ncbi_disease"}, "metrics": [{"type": "precision", "value": 0.8456973293768546, "name": "Precision"}, {"type": "recall", "value": 0.890625, "name": "Recall"}, {"type": "f1", "value": 0.8675799086757991, "name": "F1"}, {"type": "accuracy", "value": 0.9850593950279626, "name": "Accuracy"}]}]}]}
transformers tensorboard safetensors bert token-classification generated_from_trainer dataset:ncbi_disease base_model:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext license:mit model-index autotrain_compatible endpoints_compatible region:us