Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("carblacac/twitter-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pretrained("carblacac/twitter-sentiment-analysis")Quick Links
sentiment-analysis-twitter
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the new_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.4579
- Accuracy: 0.7965
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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5315 | 1.0 | 157 | 0.4517 | 0.788 |
| 0.388 | 2.0 | 314 | 0.4416 | 0.8 |
| 0.3307 | 3.0 | 471 | 0.4579 | 0.7965 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
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Space using carblacac/twitter-sentiment-analysis 1
Evaluation results
- Accuracy on new_datasetself-reported0.796
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="carblacac/twitter-sentiment-analysis")