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---
license: apache-2.0
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
datasets:
- essays_su_g
metrics:
- accuracy
model-index:
- name: longformer-spans
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: essays_su_g
type: essays_su_g
config: spans
split: test
args: spans
metrics:
- name: Accuracy
type: accuracy
value: 0.9360779676806765
---
<!-- 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 essays_su_g dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1786
- B: {'precision': 0.8126064735945485, 'recall': 0.9008498583569405, 'f1-score': 0.8544558889386475, 'support': 1059.0}
- I: {'precision': 0.9468377121729875, 'recall': 0.9617069701280228, 'f1-score': 0.9542144187884605, 'support': 17575.0}
- O: {'precision': 0.9307744259342638, 'recall': 0.8915363881401617, 'f1-score': 0.9107329698771959, 'support': 9275.0}
- Accuracy: 0.9361
- Macro avg: {'precision': 0.8967395372339334, 'recall': 0.918031072208375, 'f1-score': 0.9064677592014346, 'support': 27909.0}
- Weighted avg: {'precision': 0.9364060284323041, 'recall': 0.9360779676806765, 'f1-score': 0.9359789133327677, '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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | B | I | O | Accuracy | Macro avg | Weighted avg |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| No log | 1.0 | 41 | 0.2836 | {'precision': 0.7968253968253968, 'recall': 0.4740321057601511, 'f1-score': 0.5944345766725874, 'support': 1059.0} | {'precision': 0.9161490683229814, 'recall': 0.9399715504978663, 'f1-score': 0.9279074339315303, 'support': 17575.0} | {'precision': 0.8717421866551314, 'recall': 0.8691105121293801, 'f1-score': 0.8704243602202786, 'support': 9275.0} | 0.8987 | {'precision': 0.8615722172678364, 'recall': 0.7610380561291326, 'f1-score': 0.7975887902747987, 'support': 27909.0} | {'precision': 0.8968636193428943, 'recall': 0.8987423411802644, 'f1-score': 0.8961505359950553, 'support': 27909.0} |
| No log | 2.0 | 82 | 0.2022 | {'precision': 0.7857142857142857, 'recall': 0.8621340887629839, 'f1-score': 0.8221521837010356, 'support': 1059.0} | {'precision': 0.9355464420305919, 'recall': 0.9605120910384068, 'f1-score': 0.9478649035627053, 'support': 17575.0} | {'precision': 0.9268068482132598, 'recall': 0.8696495956873316, 'f1-score': 0.8973189453776839, 'support': 9275.0} | 0.9266 | {'precision': 0.8826891919860458, 'recall': 0.8974319251629076, 'f1-score': 0.889112010880475, 'support': 27909.0} | {'precision': 0.9269566686171867, 'recall': 0.926582822745351, 'f1-score': 0.9262968240005718, 'support': 27909.0} |
| No log | 3.0 | 123 | 0.1852 | {'precision': 0.7871125611745514, 'recall': 0.9112370160528801, 'f1-score': 0.8446389496717724, 'support': 1059.0} | {'precision': 0.9400952275495515, 'recall': 0.966145092460882, 'f1-score': 0.9529421668490613, 'support': 17575.0} | {'precision': 0.940726133859181, 'recall': 0.8743935309973045, 'f1-score': 0.9063477872150201, 'support': 9275.0} | 0.9336 | {'precision': 0.8893113075277613, 'recall': 0.9172585465036889, 'f1-score': 0.9013096345786179, 'support': 27909.0} | {'precision': 0.9345000078114988, 'recall': 0.9335698161883264, 'f1-score': 0.9333479148838716, 'support': 27909.0} |
| No log | 4.0 | 164 | 0.1786 | {'precision': 0.8126064735945485, 'recall': 0.9008498583569405, 'f1-score': 0.8544558889386475, 'support': 1059.0} | {'precision': 0.9468377121729875, 'recall': 0.9617069701280228, 'f1-score': 0.9542144187884605, 'support': 17575.0} | {'precision': 0.9307744259342638, 'recall': 0.8915363881401617, 'f1-score': 0.9107329698771959, 'support': 9275.0} | 0.9361 | {'precision': 0.8967395372339334, 'recall': 0.918031072208375, 'f1-score': 0.9064677592014346, 'support': 27909.0} | {'precision': 0.9364060284323041, 'recall': 0.9360779676806765, 'f1-score': 0.9359789133327677, 'support': 27909.0} |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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