Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use NLP-FEUP/FT-mrm8488-distilroberta-finetuned-financial-news-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NLP-FEUP/FT-mrm8488-distilroberta-finetuned-financial-news-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NLP-FEUP/FT-mrm8488-distilroberta-finetuned-financial-news-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NLP-FEUP/FT-mrm8488-distilroberta-finetuned-financial-news-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("NLP-FEUP/FT-mrm8488-distilroberta-finetuned-financial-news-sentiment-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
FT-mrm8488-distilroberta-finetuned-financial-news-sentiment-analysis
This model is a fine-tuned version of mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1981
- Accuracy: 0.925
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 40 | 0.1981 | 0.925 |
| No log | 2.0 | 80 | 0.2380 | 0.925 |
| No log | 3.0 | 120 | 0.3306 | 0.9 |
Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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