---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Longformer-finetuned-norm
  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. -->

# Longformer-finetuned-norm

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: 0.8127
- Precision: 0.8429
- Recall: 0.8701
- F1: 0.8562
- Accuracy: 0.8221

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.8008        | 1.0   | 1012 | 0.5839          | 0.8266    | 0.8637 | 0.8447 | 0.8084   |
| 0.5168        | 2.0   | 2024 | 0.5927          | 0.7940    | 0.9102 | 0.8481 | 0.8117   |
| 0.3936        | 3.0   | 3036 | 0.5651          | 0.8476    | 0.8501 | 0.8488 | 0.8143   |
| 0.2939        | 4.0   | 4048 | 0.6411          | 0.8494    | 0.8578 | 0.8536 | 0.8204   |
| 0.2165        | 5.0   | 5060 | 0.6833          | 0.8409    | 0.8822 | 0.8611 | 0.8270   |
| 0.1561        | 6.0   | 6072 | 0.7643          | 0.8404    | 0.8810 | 0.8602 | 0.8259   |
| 0.1164        | 7.0   | 7084 | 0.8127          | 0.8429    | 0.8701 | 0.8562 | 0.8221   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6