Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use ttwj-sutd/finetuning-sentiment-model-3000-samples-6pm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ttwj-sutd/finetuning-sentiment-model-3000-samples-6pm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ttwj-sutd/finetuning-sentiment-model-3000-samples-6pm")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ttwj-sutd/finetuning-sentiment-model-3000-samples-6pm") model = AutoModelForSequenceClassification.from_pretrained("ttwj-sutd/finetuning-sentiment-model-3000-samples-6pm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#3 opened almost 2 years ago
by
SFconvertbot
Librarian Bot: Add base_model information to model
#2 opened almost 3 years ago
by
librarian-bot
Align label mapping with imdb dataset
#1 opened about 4 years ago
by
lewtun