---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: SciBERT_AsymmetricLoss_25K_bs64_P1_N1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# SciBERT_AsymmetricLoss_25K_bs64_P1_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: 67.0896
- Accuracy: 0.9945
- Precision: 0.7586
- Recall: 0.6438
- F1: 0.6965
- Hamming: 0.0055

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 83.6475       | 0.16  | 5000  | 79.3653         | 0.9938   | 0.7361    | 0.5667 | 0.6404 | 0.0062  |
| 75.8712       | 0.32  | 10000 | 72.7250         | 0.9942   | 0.7513    | 0.6068 | 0.6714 | 0.0058  |
| 72.4202       | 0.47  | 15000 | 69.4174         | 0.9944   | 0.7568    | 0.6237 | 0.6838 | 0.0056  |
| 70.0693       | 0.63  | 20000 | 67.8098         | 0.9945   | 0.7561    | 0.6385 | 0.6923 | 0.0055  |
| 68.9765       | 0.79  | 25000 | 67.0896         | 0.9945   | 0.7586    | 0.6438 | 0.6965 | 0.0055  |


### Framework versions

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