---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: persuasive_essays_distilbert_cased
  results: []
language:
- en
---

# persuasive_essays_distilbert_cased

## Model description

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the [emnlp2017-claim-identification/persuasive_essays](https://github.com/UKPLab/emnlp2017-claim-identification) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4249
- Accuracy: 0.8101
- Macro F1: 0.7662
- Claim F1: 0.665

## Intended uses & limitations

Text classification for claims on full sentences. The model perfoms better at in-domain classification. Cross-domain classification is severely limited.

## Training and evaluation data

Based on [Stab and Gurevych (2017)](https://aclanthology.org/J17-3005.pdf) persuasive essays corpus, preprocessed by [Daxenberger et al. (2017)]((https://github.com/UKPLab/emnlp2017-claim-identification).

Original dataset
  - docs: 402
  - tokens: 147,271
  - total instances: 7,116 (65 duplicates)
    - #claims: 2,108 (29.62%)

Trimmed datast used for training
  - total instances: **7051** (65 duplicates removed)
    - #claims: **2093** (29.68%)
  - train/test split: 80/20, stratified

## 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Claim F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| No log        | 1.0   | 353  | 0.4369          | 0.7931   | 0.7574   | 0.6644   |
| 0.4492        | 2.0   | 706  | 0.4249          | 0.8101   | 0.7662   | 0.665    |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2