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_JNLPBA_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_JNLPBA_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: 0.1515
- Precision: 0.9549
- Recall: 0.9516
- F1: 0.9532
- Accuracy: 0.9511

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1681        | 1.0   | 1164 | 0.1500          | 0.9540    | 0.9493 | 0.9516 | 0.9492   |
| 0.1281        | 2.0   | 2328 | 0.1519          | 0.9537    | 0.9497 | 0.9517 | 0.9492   |
| 0.1019        | 3.0   | 3492 | 0.1515          | 0.9549    | 0.9516 | 0.9532 | 0.9511   |


### Framework versions

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