transformers tensorboard safetensors bert token-classification generated_from_trainer base_model:allenai/scibert_scivocab_uncased base_model:finetune:allenai/scibert_scivocab_uncased autotrain_compatible endpoints_compatible region:us
---
base_model: allenai/scibert_scivocab_uncased
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: SciBERT_CRAFT_NER_new
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# SciBERT_CRAFT_NER_new

This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1199
- Precision: 0.9743
- Recall: 0.9761
- F1: 0.9752
- Accuracy: 0.9740

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1537        | 1.0   | 695  | 0.1140          | 0.9707    | 0.9727 | 0.9717 | 0.9704   |
| 0.0452        | 2.0   | 1390 | 0.1128          | 0.9733    | 0.9750 | 0.9741 | 0.9731   |
| 0.0185        | 3.0   | 2085 | 0.1199          | 0.9743    | 0.9761 | 0.9752 | 0.9740   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
