---
license: apache-2.0
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
datasets:
- fancy_dataset
metrics:
- accuracy
model-index:
- name: longformer-spans
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: fancy_dataset
      type: fancy_dataset
      config: spans
      split: test
      args: spans
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9297359274785911
---

<!-- 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-spans

This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the fancy_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1948
- B: {'precision': 0.773955773955774, 'recall': 0.8923512747875354, 'f1-score': 0.8289473684210527, 'support': 1059.0}
- I: {'precision': 0.9401371161027814, 'recall': 0.9597155049786629, 'f1-score': 0.949825430791756, 'support': 17575.0}
- O: {'precision': 0.9301474791357037, 'recall': 0.8771967654986523, 'f1-score': 0.9028964598823661, 'support': 9275.0}
- Accuracy: 0.9297
- Macro avg: {'precision': 0.8814134563980863, 'recall': 0.9097545150882835, 'f1-score': 0.893889753031725, 'support': 27909.0}
- Weighted avg: {'precision': 0.9305115500057043, 'recall': 0.9297359274785911, 'f1-score': 0.929642834739043, 'support': 27909.0}

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                  | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 41   | 0.2901          | {'precision': 0.8336673346693386, 'recall': 0.392823418319169, 'f1-score': 0.5340179717586649, 'support': 1059.0}  | {'precision': 0.9134376209164778, 'recall': 0.9402560455192034, 'f1-score': 0.9266528346324231, 'support': 17575.0} | {'precision': 0.8671531280180277, 'recall': 0.871266846361186, 'f1-score': 0.8692051199311606, 'support': 9275.0}  | 0.8966   | {'precision': 0.8714193612012814, 'recall': 0.7347821033998527, 'f1-score': 0.7766253087740829, 'support': 27909.0} | {'precision': 0.8950290285352085, 'recall': 0.8965566663083593, 'f1-score': 0.892662800104582, 'support': 27909.0}  |
| No log        | 2.0   | 82   | 0.2109          | {'precision': 0.7553191489361702, 'recall': 0.8715769593956563, 'f1-score': 0.8092941692240245, 'support': 1059.0} | {'precision': 0.9298303409652446, 'recall': 0.9635846372688478, 'f1-score': 0.9464066167430425, 'support': 17575.0} | {'precision': 0.9366296908189757, 'recall': 0.8557412398921833, 'f1-score': 0.8943602456476422, 'support': 9275.0} | 0.9243   | {'precision': 0.8739263935734636, 'recall': 0.8969676121855624, 'f1-score': 0.883353677204903, 'support': 27909.0}  | {'precision': 0.9254681860164671, 'recall': 0.9242538249310258, 'f1-score': 0.9239073450445768, 'support': 27909.0} |
| No log        | 3.0   | 123  | 0.1948          | {'precision': 0.773955773955774, 'recall': 0.8923512747875354, 'f1-score': 0.8289473684210527, 'support': 1059.0}  | {'precision': 0.9401371161027814, 'recall': 0.9597155049786629, 'f1-score': 0.949825430791756, 'support': 17575.0}  | {'precision': 0.9301474791357037, 'recall': 0.8771967654986523, 'f1-score': 0.9028964598823661, 'support': 9275.0} | 0.9297   | {'precision': 0.8814134563980863, 'recall': 0.9097545150882835, 'f1-score': 0.893889753031725, 'support': 27909.0}  | {'precision': 0.9305115500057043, 'recall': 0.9297359274785911, 'f1-score': 0.929642834739043, 'support': 27909.0}  |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2