---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: my-finetuned-FinanceNews-distilbert
  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. -->

# my-finetuned-FinanceNews-distilbert

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3800
- Accuracy: 0.8557
- F1: 0.8547
- Precision: 0.8548
- Recall: 0.8557
- Classification Report:               precision    recall  f1-score   support

     Class 0       0.87      0.90      0.88        87
     Class 1       0.87      0.90      0.88       268
     Class 2       0.83      0.76      0.79       151

    accuracy                           0.86       506
   macro avg       0.85      0.85      0.85       506
weighted avg       0.85      0.86      0.85       506


## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Classification Report                                                                                                                                                                                                                                                                                                                                                                        |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.7555        | 1.0   | 72   | 0.4573          | 0.8241   | 0.8235 | 0.8232    | 0.8241 |               precision    recall  f1-score   support

     Class 0       0.83      0.84      0.83        87
     Class 1       0.85      0.87      0.86       268
     Class 2       0.78      0.74      0.76       151

    accuracy                           0.82       506
   macro avg       0.82      0.82      0.82       506
weighted avg       0.82      0.82      0.82       506
 |
| 0.4201        | 2.0   | 144  | 0.3800          | 0.8557   | 0.8547 | 0.8548    | 0.8557 |               precision    recall  f1-score   support

     Class 0       0.87      0.90      0.88        87
     Class 1       0.87      0.90      0.88       268
     Class 2       0.83      0.76      0.79       151

    accuracy                           0.86       506
   macro avg       0.85      0.85      0.85       506
weighted avg       0.85      0.86      0.85       506
 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.2.2
- Datasets 2.20.0
- Tokenizers 0.19.1