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("BaxterAI/SentimentClassifier")
model = AutoModelForSequenceClassification.from_pretrained("BaxterAI/SentimentClassifier")Quick Links
SentimentClassifier
This model is a fine-tuned version of distilbert-base-uncased on the amazon_polarity dataset. It achieves the following results on the evaluation set:
- Loss: 0.4425
- Accuracy: 0.91
- F1: 0.91
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: 3e-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
- num_epochs: 5
Training results
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
- Downloads last month
- 11
Dataset used to train BaxterAI/SentimentClassifier
Evaluation results
- Accuracy on amazon_polarityself-reported0.910
- F1 on amazon_polarityself-reported0.910
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaxterAI/SentimentClassifier")