---
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: 0.6204
- B-claim: {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 147.0}
- B-majorclaim: {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 77.0}
- B-premise: {'precision': 0.6069364161849711, 'recall': 0.5526315789473685, 'f1-score': 0.5785123966942148, 'support': 380.0}
- I-claim: {'precision': 0.5083841463414634, 'recall': 0.3100883310088331, 'f1-score': 0.3852151313889692, 'support': 2151.0}
- I-majorclaim: {'precision': 0.4941275167785235, 'recall': 0.562559694364852, 'f1-score': 0.5261277355962484, 'support': 1047.0}
- I-premise: {'precision': 0.8047369129323106, 'recall': 0.9100593516968498, 'f1-score': 0.8541636909012997, 'support': 6571.0}
- O: {'precision': 0.854797733046707, 'recall': 0.8704477611940299, 'f1-score': 0.862551764937882, 'support': 5025.0}
- Accuracy: 0.7676
- Macro avg: {'precision': 0.46699753218342505, 'recall': 0.45796953103027616, 'f1-score': 0.45808153135980195, 'support': 15398.0}
- Weighted avg: {'precision': 0.7419669119649179, 'recall': 0.7676321600207819, 'f1-score': 0.74985845104923, '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 | B-claim                                                              | B-majorclaim                                                        | B-premise                                                                                                          | I-claim                                                                                                              | I-majorclaim                                                                                                        | I-premise                                                                                                          | O                                                                                                                  | Accuracy | Macro avg                                                                                                              | Weighted avg                                                                                                        |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------:|:-------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:----------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 36   | 0.8375          | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 147.0} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 77.0} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 380.0}                                               | {'precision': 0.3307174887892377, 'recall': 0.13714551371455136, 'f1-score': 0.1938876109102859, 'support': 2151.0}  | {'precision': 0.25, 'recall': 0.0009551098376313276, 'f1-score': 0.0019029495718363464, 'support': 1047.0}          | {'precision': 0.6899198931909212, 'recall': 0.9436919799117334, 'f1-score': 0.7970949289800116, 'support': 6571.0} | {'precision': 0.7767500906782735, 'recall': 0.8523383084577114, 'f1-score': 0.8127905873422525, 'support': 5025.0} | 0.7001   | {'precision': 0.29248392466549034, 'recall': 0.2763044159888039, 'f1-score': 0.25795372525776944, 'support': 15398.0}  | {'precision': 0.6111024900767319, 'recall': 0.7000909208988181, 'f1-score': 0.6326164514217569, 'support': 15398.0} |
| No log        | 2.0   | 72   | 0.6930          | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 147.0} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 77.0} | {'precision': 0.6489795918367347, 'recall': 0.41842105263157897, 'f1-score': 0.5087999999999999, 'support': 380.0} | {'precision': 0.4376956793988729, 'recall': 0.32496513249651326, 'f1-score': 0.37299893276414087, 'support': 2151.0} | {'precision': 0.3946384039900249, 'recall': 0.6045845272206304, 'f1-score': 0.47755563938136547, 'support': 1047.0} | {'precision': 0.83792191631669, 'recall': 0.8198143357175468, 'f1-score': 0.8287692307692307, 'support': 6571.0}   | {'precision': 0.8073510773130546, 'recall': 0.887363184079602, 'f1-score': 0.8454683352294274, 'support': 5025.0}  | 0.7363   | {'precision': 0.446655238407911, 'recall': 0.4364497474494102, 'f1-score': 0.4333703054491663, 'support': 15398.0}     | {'precision': 0.7250426117598104, 'recall': 0.7362644499285621, 'f1-score': 0.7267168761345917, 'support': 15398.0} |
| No log        | 3.0   | 108  | 0.6204          | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 147.0} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 77.0} | {'precision': 0.6069364161849711, 'recall': 0.5526315789473685, 'f1-score': 0.5785123966942148, 'support': 380.0}  | {'precision': 0.5083841463414634, 'recall': 0.3100883310088331, 'f1-score': 0.3852151313889692, 'support': 2151.0}   | {'precision': 0.4941275167785235, 'recall': 0.562559694364852, 'f1-score': 0.5261277355962484, 'support': 1047.0}   | {'precision': 0.8047369129323106, 'recall': 0.9100593516968498, 'f1-score': 0.8541636909012997, 'support': 6571.0} | {'precision': 0.854797733046707, 'recall': 0.8704477611940299, 'f1-score': 0.862551764937882, 'support': 5025.0}   | 0.7676   | {'precision': 0.46699753218342505, 'recall': 0.45796953103027616, 'f1-score': 0.45808153135980195, 'support': 15398.0} | {'precision': 0.7419669119649179, 'recall': 0.7676321600207819, 'f1-score': 0.74985845104923, 'support': 15398.0}   |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3