Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use LogischeIP/SentimentT2_BertBase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LogischeIP/SentimentT2_BertBase with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LogischeIP/SentimentT2_BertBase")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LogischeIP/SentimentT2_BertBase") model = AutoModelForSequenceClassification.from_pretrained("LogischeIP/SentimentT2_BertBase", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d391520292bc345e326deda9353ebe3ecfa64d81ed5aba9edfb67ce2c3633576
- Size of remote file:
- 438 MB
- SHA256:
- c9bf8e95223d2c8f219533265f32b534ff637011aa71a1a4ee7efae4215e9fcd
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