Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use AndreiUrsu/TweetRobertaNewDataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndreiUrsu/TweetRobertaNewDataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AndreiUrsu/TweetRobertaNewDataset")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AndreiUrsu/TweetRobertaNewDataset") model = AutoModelForSequenceClassification.from_pretrained("AndreiUrsu/TweetRobertaNewDataset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from AndreiUrsu/TweetRobertaNewDataset: direct link, hf CLI and curl.
- Browser
- Download file 1.73 kB
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https://huggingface.co/AndreiUrsu/TweetRobertaNewDataset/resolve/main/README.md
- Command line
-
hf download hf://AndreiUrsu/TweetRobertaNewDataset/README.md
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curl -L -o README.md https://huggingface.co/AndreiUrsu/TweetRobertaNewDataset/resolve/main/README.md
1.73 kB
metadata
base_model: AndreiUrsu/TweetRoberta_5epochs
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: TweetRobertaNewDataset
results: []
TweetRobertaNewDataset
This model is a fine-tuned version of AndreiUrsu/TweetRoberta_5epochs on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- F1: 1.0
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.0 | 1.0 | 1000 | 0.0000 | 1.0 | 1.0 |
| 0.0 | 2.0 | 2000 | 0.0000 | 1.0 | 1.0 |
| 0.0 | 3.0 | 3000 | 0.0000 | 1.0 | 1.0 |
| 0.0 | 4.0 | 4000 | 0.0000 | 1.0 | 1.0 |
| 0.0 | 5.0 | 5000 | 0.0000 | 1.0 | 1.0 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1