Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("penscola/tweet_sentiments_analysis_distilbert")
model = AutoModelForSequenceClassification.from_pretrained("penscola/tweet_sentiments_analysis_distilbert")Quick Links
tweet_sentiments_analysis_distilbert
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5879
- F1-score: 0.7623
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: 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1-score |
|---|---|---|---|---|
| 0.6918 | 1.0 | 1000 | 0.6804 | 0.6942 |
| 0.5882 | 2.0 | 2000 | 0.5879 | 0.7623 |
| 0.4611 | 3.0 | 3000 | 0.6322 | 0.7650 |
| 0.3188 | 4.0 | 4000 | 0.9293 | 0.7634 |
| 0.2073 | 5.0 | 5000 | 1.1295 | 0.7673 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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Model tree for penscola/tweet_sentiments_analysis_distilbert
Base model
distilbert/distilbert-base-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="penscola/tweet_sentiments_analysis_distilbert")