Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use khygopole/NLP_HerbalMultilabelClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khygopole/NLP_HerbalMultilabelClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="khygopole/NLP_HerbalMultilabelClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("khygopole/NLP_HerbalMultilabelClassification") model = AutoModelForSequenceClassification.from_pretrained("khygopole/NLP_HerbalMultilabelClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8531f19ae97a05eda4b2b697897597092cb5d4b7cd0d70711f708c24151bc095
- Size of remote file:
- 4.73 kB
- SHA256:
- 95ad2eee12bcda8026a061008edbe413c6659e8a0ffd4a736dcc812c67a72dde
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