---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-ner-essays-classify_span
  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. -->

# bert-ner-essays-classify_span

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6951
- Claim: {'precision': 0.4811320754716981, 'recall': 0.3541666666666667, 'f1-score': 0.408, 'support': 144.0}
- Majorclaim: {'precision': 0.625, 'recall': 0.4861111111111111, 'f1-score': 0.5468749999999999, 'support': 72.0}
- Premise: {'precision': 0.7718120805369127, 'recall': 0.8778625954198473, 'f1-score': 0.8214285714285714, 'support': 393.0}
- Accuracy: 0.7077
- Macro avg: {'precision': 0.6259813853362036, 'recall': 0.5727134577325418, 'f1-score': 0.5921011904761905, 'support': 609.0}
- Weighted avg: {'precision': 0.6857227693250102, 'recall': 0.7077175697865353, 'f1-score': 0.6912125263898662, 'support': 609.0}

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                                | Majorclaim                                                                                          | Premise                                                                                                           | Accuracy | Macro avg                                                                                                         | Weighted avg                                                                                                      |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:--------:|:-----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 267  | 0.7245          | {'precision': 0.35714285714285715, 'recall': 0.10416666666666667, 'f1-score': 0.16129032258064516, 'support': 144.0} | {'precision': 0.5806451612903226, 'recall': 0.25, 'f1-score': 0.34951456310679613, 'support': 72.0} | {'precision': 0.6940298507462687, 'recall': 0.9465648854961832, 'f1-score': 0.8008611410118407, 'support': 393.0} | 0.6650   | {'precision': 0.5439392897264828, 'recall': 0.4335771840542833, 'f1-score': 0.4372220088997607, 'support': 609.0} | {'precision': 0.6009667559684043, 'recall': 0.6650246305418719, 'f1-score': 0.5962714013349024, 'support': 609.0} |
| 0.7275        | 2.0   | 534  | 0.6951          | {'precision': 0.4811320754716981, 'recall': 0.3541666666666667, 'f1-score': 0.408, 'support': 144.0}                 | {'precision': 0.625, 'recall': 0.4861111111111111, 'f1-score': 0.5468749999999999, 'support': 72.0} | {'precision': 0.7718120805369127, 'recall': 0.8778625954198473, 'f1-score': 0.8214285714285714, 'support': 393.0} | 0.7077   | {'precision': 0.6259813853362036, 'recall': 0.5727134577325418, 'f1-score': 0.5921011904761905, 'support': 609.0} | {'precision': 0.6857227693250102, 'recall': 0.7077175697865353, 'f1-score': 0.6912125263898662, 'support': 609.0} |


### Framework versions

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