---
library_name: peft
tags:
- generated_from_trainer
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
metrics:
- accuracy
- precision
- recall
model-index:
- name: twitter-roberta-base-sentiment-latest-biden-stance-1
  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. -->

# twitter-roberta-base-sentiment-latest-biden-stance-1

This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4037
- Accuracy: {'accuracy': 0.5688073394495413}
- Precision: {'precision': 0.5540838852097131}
- Recall: {'recall': 0.6640211640211641}
- F1 Score: {'f1': 0.6040914560770156}

## 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: 0.001
- 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy               | Precision                         | Recall              | F1 Score                   |
|:-------------:|:-----:|:-----:|:---------------:|:----------------------:|:---------------------------------:|:-------------------:|:--------------------------:|
| 0.4339        | 1.0   | 3600  | 0.4173          | {'accuracy': 0.8925}   | {'precision': 0.857630979498861}  | {'recall': 0.94125} | {'f1': 0.8974970202622169} |
| 0.3848        | 2.0   | 7200  | 0.5757          | {'accuracy': 0.854375} | {'precision': 0.9341500765696784} | {'recall': 0.7625}  | {'f1': 0.8396421197522368} |
| 0.4094        | 3.0   | 10800 | 0.3543          | {'accuracy': 0.904375} | {'precision': 0.8655367231638418} | {'recall': 0.9575}  | {'f1': 0.9091988130563798} |
| 0.3937        | 4.0   | 14400 | 0.2576          | {'accuracy': 0.91125}  | {'precision': 0.9092039800995025} | {'recall': 0.91375} | {'f1': 0.9114713216957606} |
| 0.3401        | 5.0   | 18000 | 0.2671          | {'accuracy': 0.91625}  | {'precision': 0.9291237113402062} | {'recall': 0.90125} | {'f1': 0.9149746192893401} |
| 0.352         | 6.0   | 21600 | 0.2429          | {'accuracy': 0.91875}  | {'precision': 0.9294871794871795} | {'recall': 0.90625} | {'f1': 0.9177215189873418} |
| 0.2883        | 7.0   | 25200 | 0.2857          | {'accuracy': 0.915625} | {'precision': 0.917189460476788}  | {'recall': 0.91375} | {'f1': 0.915466499686913}  |
| 0.2894        | 8.0   | 28800 | 0.2270          | {'accuracy': 0.92375}  | {'precision': 0.9302030456852792} | {'recall': 0.91625} | {'f1': 0.9231738035264484} |
| 0.282         | 9.0   | 32400 | 0.2518          | {'accuracy': 0.92}     | {'precision': 0.9189526184538653} | {'recall': 0.92125} | {'f1': 0.920099875156055}  |
| 0.2485        | 10.0  | 36000 | 0.2351          | {'accuracy': 0.92375}  | {'precision': 0.9269521410579346} | {'recall': 0.92}    | {'f1': 0.9234629861982434} |


### Framework versions

- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2