---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: test_AsymmetricLoss_25K_bs64_P4_N1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# test_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: 0.6203
- Accuracy: 0.7448
- Precision: 0.0101
- Recall: 0.2592
- F1: 0.0194
- Hamming: 0.2552

## 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: 40
- eval_batch_size: 40
- 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Hamming |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.6901        | 0.0   | 5    | 0.6457          | 0.6626   | 0.0099    | 0.3394 | 0.0192 | 0.3374  |
| 0.6344        | 0.0   | 10   | 0.6203          | 0.7448   | 0.0101    | 0.2592 | 0.0194 | 0.2552  |


### Framework versions

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