---
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: 0.9372962126912465
---

<!-- 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
- : {'precision': 0.9405594405594405, 'recall': 0.9672104754749383, 'f1-score': 0.9536988041062546, 'support': 18634.0}
- O: {'precision': 0.9301474791357037, 'recall': 0.8771967654986523, 'f1-score': 0.9028964598823661, 'support': 9275.0}
- Accuracy: 0.9373
- Macro avg: {'precision': 0.9353534598475721, 'recall': 0.9222036204867954, 'f1-score': 0.9282976319943104, 'support': 27909.0}
- Weighted avg: {'precision': 0.9370992326621616, 'recall': 0.9372962126912465, 'f1-score': 0.9368156573551505, '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 |                                                                                                                     | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 41   | 0.2901          | {'precision': 0.9357719203873051, 'recall': 0.9335623054631319, 'f1-score': 0.9346658070062326, 'support': 18634.0} | {'precision': 0.8671531280180277, 'recall': 0.871266846361186, 'f1-score': 0.8692051199311606, 'support': 9275.0}  | 0.9129   | {'precision': 0.9014625242026664, 'recall': 0.9024145759121589, 'f1-score': 0.9019354634686966, 'support': 27909.0} | {'precision': 0.9129678321281397, 'recall': 0.9128596510086352, 'f1-score': 0.9129112521091997, 'support': 27909.0} |
| No log        | 2.0   | 82   | 0.2109          | {'precision': 0.9311551324929251, 'recall': 0.9711817108511324, 'f1-score': 0.9507473272216239, 'support': 18634.0} | {'precision': 0.9366296908189757, 'recall': 0.8557412398921833, 'f1-score': 0.8943602456476422, 'support': 9275.0} | 0.9328   | {'precision': 0.9338924116559504, 'recall': 0.9134614753716579, 'f1-score': 0.922553786434633, 'support': 27909.0}  | {'precision': 0.9329744928596212, 'recall': 0.9328173707406213, 'f1-score': 0.9320082043007497, 'support': 27909.0} |
| No log        | 3.0   | 123  | 0.1948          | {'precision': 0.9405594405594405, 'recall': 0.9672104754749383, 'f1-score': 0.9536988041062546, 'support': 18634.0} | {'precision': 0.9301474791357037, 'recall': 0.8771967654986523, 'f1-score': 0.9028964598823661, 'support': 9275.0} | 0.9373   | {'precision': 0.9353534598475721, 'recall': 0.9222036204867954, 'f1-score': 0.9282976319943104, 'support': 27909.0} | {'precision': 0.9370992326621616, 'recall': 0.9372962126912465, 'f1-score': 0.9368156573551505, 'support': 27909.0} |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1