---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: SciBERT_BioNLP13CG_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_BioNLP13CG_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: 0.1760
- Precision: 0.9560
- Recall: 0.9595
- F1: 0.9577
- Accuracy: 0.9560

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 191  | 0.1970          | 0.9469    | 0.9507 | 0.9488 | 0.9474   |
| No log        | 2.0   | 382  | 0.1724          | 0.9544    | 0.9584 | 0.9564 | 0.9551   |
| 0.2509        | 3.0   | 573  | 0.1760          | 0.9560    | 0.9595 | 0.9577 | 0.9560   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0