Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use LogischeIP/SentimentT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LogischeIP/SentimentT2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LogischeIP/SentimentT2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LogischeIP/SentimentT2") model = AutoModelForSequenceClassification.from_pretrained("LogischeIP/SentimentT2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a6479026794ffdc72a6b858ada03035e1491dff7bc487f02960e94b13720f21
- Size of remote file:
- 268 MB
- SHA256:
- 807e73b7e11d71e4c25edd71509c91e25d6055f4dfa6fdde76d6e62f8b4ffd4a
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