---
license: apache-2.0
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: longformer-one-step
  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-one-step

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: 0.5640
- Claim: {'precision': 0.5519765739385066, 'recall': 0.32811140121845084, 'f1-score': 0.41157205240174677, 'support': 2298.0}
- Majorclaim: {'precision': 0.5541490857946554, 'recall': 0.701067615658363, 'f1-score': 0.6190102120974077, 'support': 1124.0}
- O: {'precision': 0.8899137758171245, 'recall': 0.8831840796019901, 'f1-score': 0.8865361566120655, 'support': 5025.0}
- Premise: {'precision': 0.830119375573921, 'recall': 0.9103726082578046, 'f1-score': 0.8683957732949088, 'support': 6951.0}
- Accuracy: 0.7993
- Macro avg: {'precision': 0.7065397027810518, 'recall': 0.7056839261841521, 'f1-score': 0.6963785486015321, 'support': 15398.0}
- Weighted avg: {'precision': 0.7879778050681424, 'recall': 0.7993245876087803, 'f1-score': 0.7879350085702844, 'support': 15398.0}

## 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 | Claim                                                                                                                 | Majorclaim                                                                                                             | O                                                                                                                  | Premise                                                                                                            | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 36   | 0.7525          | {'precision': 0.41766381766381766, 'recall': 0.31897302001740646, 'f1-score': 0.36170737725141877, 'support': 2298.0} | {'precision': 0.43548387096774194, 'recall': 0.02402135231316726, 'f1-score': 0.045531197301854974, 'support': 1124.0} | {'precision': 0.7476681394207167, 'recall': 0.9092537313432836, 'f1-score': 0.8205818965517241, 'support': 5025.0} | {'precision': 0.8187416331994646, 'recall': 0.8798733995108617, 'f1-score': 0.8482074752097636, 'support': 6951.0} | 0.7433   | {'precision': 0.6048893653129352, 'recall': 0.5330303757961797, 'f1-score': 0.5190069865786904, 'support': 15398.0} | {'precision': 0.707714041883217, 'recall': 0.7432783478373814, 'f1-score': 0.7079942076273884, 'support': 15398.0}  |
| No log        | 2.0   | 72   | 0.6577          | {'precision': 0.4793814432989691, 'recall': 0.3237597911227154, 'f1-score': 0.38649350649350644, 'support': 2298.0}   | {'precision': 0.41677503250975295, 'recall': 0.5702846975088968, 'f1-score': 0.48159278737791134, 'support': 1124.0}   | {'precision': 0.7966573816155988, 'recall': 0.9106467661691542, 'f1-score': 0.849846782431052, 'support': 5025.0}  | {'precision': 0.8743144424131627, 'recall': 0.8256365990504964, 'f1-score': 0.8492785793562707, 'support': 6951.0} | 0.7598   | {'precision': 0.6417820749593709, 'recall': 0.6575819634628157, 'f1-score': 0.6418029139146851, 'support': 15398.0} | {'precision': 0.7566331163186304, 'recall': 0.7598389401220937, 'f1-score': 0.7535581151939423, 'support': 15398.0} |
| No log        | 3.0   | 108  | 0.5640          | {'precision': 0.5519765739385066, 'recall': 0.32811140121845084, 'f1-score': 0.41157205240174677, 'support': 2298.0}  | {'precision': 0.5541490857946554, 'recall': 0.701067615658363, 'f1-score': 0.6190102120974077, 'support': 1124.0}      | {'precision': 0.8899137758171245, 'recall': 0.8831840796019901, 'f1-score': 0.8865361566120655, 'support': 5025.0} | {'precision': 0.830119375573921, 'recall': 0.9103726082578046, 'f1-score': 0.8683957732949088, 'support': 6951.0}  | 0.7993   | {'precision': 0.7065397027810518, 'recall': 0.7056839261841521, 'f1-score': 0.6963785486015321, 'support': 15398.0} | {'precision': 0.7879778050681424, 'recall': 0.7993245876087803, 'f1-score': 0.7879350085702844, 'support': 15398.0} |


### Framework versions

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