---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: SciBERT_JNLPBA_NER
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# SciBERT_JNLPBA_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: 0.1456
- Precision: 0.8042
- Recall: 0.8228
- F1: 0.8134
- Accuracy: 0.9512

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.234         | 1.0   | 582  | 0.1536          | 0.7820    | 0.7944 | 0.7882 | 0.9469   |
| 0.1398        | 2.0   | 1164 | 0.1489          | 0.7962    | 0.8033 | 0.7997 | 0.9495   |
| 0.1212        | 3.0   | 1746 | 0.1456          | 0.8042    | 0.8228 | 0.8134 | 0.9512   |


### Framework versions

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