---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: SciBERT_AsymmetricLoss_25K_bs64_P4_N1
  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_AsymmetricLoss_25K_bs64_P4_N1

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: 30.2502
- Accuracy: 0.9871
- Precision: 0.4247
- Recall: 0.8998
- F1: 0.5770
- Hamming: 0.0129

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 36.6287       | 0.16  | 5000  | 34.9978         | 0.9852   | 0.3863    | 0.8728 | 0.5355 | 0.0148  |
| 33.8929       | 0.32  | 10000 | 32.4942         | 0.9857   | 0.3958    | 0.8901 | 0.5480 | 0.0143  |
| 32.5419       | 0.47  | 15000 | 31.3170         | 0.9867   | 0.4162    | 0.8941 | 0.5680 | 0.0133  |
| 31.565        | 0.63  | 20000 | 30.6092         | 0.9869   | 0.4201    | 0.8975 | 0.5723 | 0.0131  |
| 31.105        | 0.79  | 25000 | 30.2502         | 0.9871   | 0.4247    | 0.8998 | 0.5770 | 0.0129  |


### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.14.1