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
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# SciBERT_CRAFT_NER

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: 0.1151
- Precision: 0.8587
- Recall: 0.8688
- F1: 0.8637
- Accuracy: 0.9733

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 347  | 0.1181          | 0.8302    | 0.8514 | 0.8407 | 0.9688   |
| 0.1267        | 2.0   | 695  | 0.1140          | 0.8435    | 0.8659 | 0.8546 | 0.9717   |
| 0.0323        | 3.0   | 1041 | 0.1151          | 0.8587    | 0.8688 | 0.8637 | 0.9733   |


### Framework versions

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