Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use vmmalvarez/bert_classifier_llmhum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vmmalvarez/bert_classifier_llmhum with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vmmalvarez/bert_classifier_llmhum") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use vmmalvarez/bert_classifier_llmhum with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("vmmalvarez/bert_classifier_llmhum") model = AutoModel.from_pretrained("vmmalvarez/bert_classifier_llmhum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f99853893701f4c6f31868914c5108106f2438bb83cb61fe51cc58f3f6d72a88
- Size of remote file:
- 438 MB
- SHA256:
- a52b2652d5c2a9ace747500fceb496ab2994f21d1a6f681584b40ef1a7edbac6
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