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---
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
datasets:
- essays_su_g
metrics:
- accuracy
model-index:
- name: longformer-sep_tok
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: essays_su_g
type: essays_su_g
config: sep_tok
split: train[80%:100%]
args: sep_tok
metrics:
- name: Accuracy
type: accuracy
value: 0.888301632721764
---
<!-- 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-sep_tok
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2543
- Claim: {'precision': 0.6146161934805467, 'recall': 0.5609404990403071, 'f1-score': 0.5865529352734571, 'support': 4168.0}
- Majorclaim: {'precision': 0.8342541436464088, 'recall': 0.8420074349442379, 'f1-score': 0.8381128584643849, 'support': 2152.0}
- O: {'precision': 0.9999115122555526, 'recall': 0.998939179632249, 'f1-score': 0.9994251094503163, 'support': 11312.0}
- Premise: {'precision': 0.8800289668490505, 'recall': 0.9059057400811729, 'f1-score': 0.8927798865352434, 'support': 12073.0}
- Accuracy: 0.8883
- Macro avg: {'precision': 0.8322027040578897, 'recall': 0.8269482134244918, 'f1-score': 0.8292176974308504, 'support': 29705.0}
- Weighted avg: {'precision': 0.8851245229744956, 'recall': 0.888301632721764, 'f1-score': 0.8864635554242416, 'support': 29705.0}
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Claim | Majorclaim | O | Premise | Accuracy | Macro avg | Weighted avg |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| No log | 1.0 | 41 | 0.4315 | {'precision': 0.3877917414721723, 'recall': 0.2072936660268714, 'f1-score': 0.2701688555347092, 'support': 4168.0} | {'precision': 0.6876106194690266, 'recall': 0.36105947955390333, 'f1-score': 0.473491773308958, 'support': 2152.0} | {'precision': 0.9996342021033379, 'recall': 0.9663189533239038, 'f1-score': 0.982694295860116, 'support': 11312.0} | {'precision': 0.7669997404619777, 'recall': 0.9791269775532179, 'f1-score': 0.8601782790613062, 'support': 12073.0} | 0.8212 | {'precision': 0.7105090758766286, 'recall': 0.6284497691144741, 'f1-score': 0.6466333009412724, 'support': 29705.0} | {'precision': 0.7966303313362657, 'recall': 0.8211748863827638, 'f1-score': 0.7960339445852997, 'support': 29705.0} |
| No log | 2.0 | 82 | 0.2780 | {'precision': 0.6238676644348169, 'recall': 0.3800383877159309, 'f1-score': 0.4723423289100939, 'support': 4168.0} | {'precision': 0.7306525037936267, 'recall': 0.8949814126394052, 'f1-score': 0.8045112781954887, 'support': 2152.0} | {'precision': 0.9998226164079823, 'recall': 0.996552333804809, 'f1-score': 0.9981847965643954, 'support': 11312.0} | {'precision': 0.8501697472651829, 'recall': 0.9334051188602667, 'f1-score': 0.8898452305748579, 'support': 12073.0} | 0.8770 | {'precision': 0.8011281329754022, 'recall': 0.8012443132551029, 'f1-score': 0.791220908561209, 'support': 29705.0} | {'precision': 0.8667475983527302, 'recall': 0.8770240700218819, 'f1-score': 0.866338966000359, 'support': 29705.0} |
| No log | 3.0 | 123 | 0.2682 | {'precision': 0.6295336787564767, 'recall': 0.46641074856046066, 'f1-score': 0.535832414553473, 'support': 4168.0} | {'precision': 0.7360406091370558, 'recall': 0.9433085501858736, 'f1-score': 0.8268839103869654, 'support': 2152.0} | {'precision': 0.9999115200849407, 'recall': 0.9990275813295615, 'f1-score': 0.9994693552666489, 'support': 11312.0} | {'precision': 0.8739348570518436, 'recall': 0.9089704298848671, 'f1-score': 0.8911084043848965, 'support': 12073.0} | 0.8837 | {'precision': 0.8098551662575791, 'recall': 0.8294293274901907, 'f1-score': 0.8133235211479959, 'support': 29705.0} | {'precision': 0.8776256659925162, 'recall': 0.8836559501767379, 'f1-score': 0.8778708228219765, 'support': 29705.0} |
| No log | 4.0 | 164 | 0.2543 | {'precision': 0.6146161934805467, 'recall': 0.5609404990403071, 'f1-score': 0.5865529352734571, 'support': 4168.0} | {'precision': 0.8342541436464088, 'recall': 0.8420074349442379, 'f1-score': 0.8381128584643849, 'support': 2152.0} | {'precision': 0.9999115122555526, 'recall': 0.998939179632249, 'f1-score': 0.9994251094503163, 'support': 11312.0} | {'precision': 0.8800289668490505, 'recall': 0.9059057400811729, 'f1-score': 0.8927798865352434, 'support': 12073.0} | 0.8883 | {'precision': 0.8322027040578897, 'recall': 0.8269482134244918, 'f1-score': 0.8292176974308504, 'support': 29705.0} | {'precision': 0.8851245229744956, 'recall': 0.888301632721764, 'f1-score': 0.8864635554242416, 'support': 29705.0} |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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