---
license: mit
tags:
- generated_from_trainer
model-index:
- name: verdict-classifier
  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. -->

# verdict-classifier

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1573
- F1 Macro: 0.0550
- F1 Misinformation: 0.0
- F1 Factual: 0.1650
- F1 Other: 0.0
- Prec Macro: 0.0300
- Prec Misinformation: 0.0
- Prec Factual: 0.0899
- Prec Other: 0.0

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 162525
- num_epochs: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Misinformation | F1 Factual | F1 Other | Prec Macro | Prec Misinformation | Prec Factual | Prec Other |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:----------:|:--------:|:----------:|:-------------------:|:------------:|:----------:|
| 1.2021        | 0.0   | 50   | 1.1573          | 0.0550   | 0.0               | 0.1650     | 0.0      | 0.0300     | 0.0                 | 0.0899       | 0.0        |
| 1.1948        | 0.0   | 100  | 1.1569          | 0.0550   | 0.0               | 0.1650     | 0.0      | 0.0300     | 0.0                 | 0.0899       | 0.0        |
| 1.1968        | 0.01  | 150  | 1.1563          | 0.0550   | 0.0               | 0.1650     | 0.0      | 0.0300     | 0.0                 | 0.0899       | 0.0        |
| 1.1925        | 0.01  | 200  | 1.1554          | 0.0550   | 0.0               | 0.1650     | 0.0      | 0.0300     | 0.0                 | 0.0899       | 0.0        |
| 1.2055        | 0.01  | 250  | 1.1544          | 0.0550   | 0.0               | 0.1650     | 0.0      | 0.0300     | 0.0                 | 0.0899       | 0.0        |
| 1.1927        | 0.01  | 300  | 1.1531          | 0.0550   | 0.0               | 0.1650     | 0.0      | 0.0300     | 0.0                 | 0.0899       | 0.0        |
| 1.1923        | 0.02  | 350  | 1.1515          | 0.0550   | 0.0               | 0.1650     | 0.0      | 0.0300     | 0.0                 | 0.0899       | 0.0        |
| 1.1929        | 0.02  | 400  | 1.1496          | 0.0550   | 0.0               | 0.1650     | 0.0      | 0.0300     | 0.0                 | 0.0899       | 0.0        |
| 1.1924        | 0.02  | 450  | 1.1476          | 0.0550   | 0.0               | 0.1650     | 0.0      | 0.0300     | 0.0                 | 0.0899       | 0.0        |
| 1.1862        | 0.02  | 500  | 1.1454          | 0.0550   | 0.0               | 0.1650     | 0.0      | 0.0300     | 0.0                 | 0.0899       | 0.0        |
| 1.1781        | 0.03  | 550  | 1.1432          | 0.0550   | 0.0               | 0.1650     | 0.0      | 0.0300     | 0.0                 | 0.0899       | 0.0        |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0+cu102
- Datasets 1.9.0
- Tokenizers 0.10.2