---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Bert_v5
  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. -->

# Bert_v5

This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9191
- Precision: 0.7612
- Recall: 0.8007
- F1: 0.5106
- Accuracy: 0.7357

## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.0663        | 1.0   | 934  | 0.8636          | 0.6973    | 0.8467 | 0.4082 | 0.7023   |
| 0.8354        | 2.0   | 1868 | 0.8261          | 0.7367    | 0.8086 | 0.4733 | 0.7221   |
| 0.7164        | 3.0   | 2802 | 0.7737          | 0.7572    | 0.7988 | 0.5055 | 0.7347   |
| 0.6149        | 4.0   | 3736 | 0.7542          | 0.7488    | 0.8402 | 0.5176 | 0.7438   |
| 0.5153        | 5.0   | 4670 | 0.8185          | 0.7614    | 0.8123 | 0.5017 | 0.7389   |
| 0.4314        | 6.0   | 5604 | 0.8599          | 0.7543    | 0.8259 | 0.5085 | 0.7395   |
| 0.3689        | 7.0   | 6538 | 0.9191          | 0.7612    | 0.8007 | 0.5106 | 0.7357   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1