---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: email_question_extraction
  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. -->

# email_question_extraction

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.3143
- Recall: 0.7097
- F1: 0.4356
- Accuracy: 0.9700

## 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
- 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1978        | 1.0   | 91   | nan             | 0.2462    | 0.5161 | 0.3333 | 0.9763   |
| 0.1749        | 2.0   | 182  | nan             | 0.3182    | 0.6774 | 0.4330 | 0.9681   |
| 0.0351        | 3.0   | 273  | nan             | 0.2647    | 0.5806 | 0.3636 | 0.9779   |
| 0.0434        | 4.0   | 364  | nan             | 0.3143    | 0.7097 | 0.4356 | 0.9700   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.16.0
- Tokenizers 0.15.0