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

# finetuned_robert

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the topic-keyword inclusion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2694
- F1: 0.9041
- Precision: 0.8354
- Recall: 0.9851
- Accuracy: 0.9067

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 0.7067        | 0.28  | 10   | 0.6843          | 0.0    | 0.0       | 0.0    | 0.5533   |
| 0.7087        | 0.56  | 20   | 0.6786          | 0.0    | 0.0       | 0.0    | 0.5533   |
| 0.6887        | 0.83  | 30   | 0.6543          | 0.7241 | 0.8571    | 0.6269 | 0.7867   |
| 0.6773        | 1.11  | 40   | 0.6069          | 0.816  | 0.8793    | 0.7612 | 0.8467   |
| 0.6073        | 1.39  | 50   | 0.4951          | 0.7711 | 0.6465    | 0.9552 | 0.7467   |
| 0.5731        | 1.67  | 60   | 0.3976          | 0.8219 | 0.7595    | 0.8955 | 0.8267   |
| 0.4806        | 1.94  | 70   | 0.3487          | 0.8421 | 0.8485    | 0.8358 | 0.86     |
| 0.4685        | 2.22  | 80   | 0.5218          | 0.7811 | 0.6471    | 0.9851 | 0.7533   |
| 0.4243        | 2.5   | 90   | 0.8471          | 0.7322 | 0.5776    | 1.0    | 0.6733   |
| 0.3692        | 2.78  | 100  | 0.3453          | 0.8514 | 0.7778    | 0.9403 | 0.8533   |
| 0.4633        | 3.06  | 110  | 0.2813          | 0.8611 | 0.8052    | 0.9254 | 0.8667   |
| 0.3334        | 3.33  | 120  | 0.3090          | 0.8514 | 0.7778    | 0.9403 | 0.8533   |
| 0.3167        | 3.61  | 130  | 0.3531          | 0.8497 | 0.7558    | 0.9701 | 0.8467   |
| 0.2615        | 3.89  | 140  | 0.2679          | 0.8873 | 0.84      | 0.9403 | 0.8933   |
| 0.2672        | 4.17  | 150  | 0.2528          | 0.8889 | 0.8312    | 0.9552 | 0.8933   |
| 0.2103        | 4.44  | 160  | 0.2905          | 0.8649 | 0.7901    | 0.9552 | 0.8667   |
| 0.2208        | 4.72  | 170  | 0.2992          | 0.8649 | 0.7901    | 0.9552 | 0.8667   |
| 0.2267        | 5.0   | 180  | 0.2911          | 0.8859 | 0.8049    | 0.9851 | 0.8867   |
| 0.1623        | 5.28  | 190  | 0.2355          | 0.9014 | 0.8533    | 0.9552 | 0.9067   |
| 0.2148        | 5.56  | 200  | 0.2200          | 0.9091 | 0.8553    | 0.9701 | 0.9133   |
| 0.1537        | 5.83  | 210  | 0.2694          | 0.9041 | 0.8354    | 0.9851 | 0.9067   |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2