---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: SciBERT_25K_steps_higherlr_bs64
  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_25K_steps_higherlr_bs64

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: 0.0485
- Accuracy: 0.9902
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Hamming: 0.0098

## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 25000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1  | Hamming |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:---:|:-------:|
| 0.0488        | 0.16  | 5000  | 0.0489          | 0.9902   | 0.0       | 0.0    | 0.0 | 0.0098  |
| 0.0486        | 0.32  | 10000 | 0.0488          | 0.9902   | 0.0       | 0.0    | 0.0 | 0.0098  |
| 0.0484        | 0.47  | 15000 | 0.0488          | 0.9902   | 0.0       | 0.0    | 0.0 | 0.0098  |
| 0.0482        | 0.63  | 20000 | 0.0485          | 0.9902   | 0.0       | 0.0    | 0.0 | 0.0098  |
| 0.0481        | 0.79  | 25000 | 0.0485          | 0.9902   | 0.0       | 0.0    | 0.0 | 0.0098  |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3