Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use omarelsayeed/DISASTER_MODEL_PRECHATS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use omarelsayeed/DISASTER_MODEL_PRECHATS with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("omarelsayeed/DISASTER_MODEL_PRECHATS") 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 omarelsayeed/DISASTER_MODEL_PRECHATS with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("omarelsayeed/DISASTER_MODEL_PRECHATS") model = AutoModel.from_pretrained("omarelsayeed/DISASTER_MODEL_PRECHATS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from omarelsayeed/DISASTER_MODEL_PRECHATS: direct link, hf CLI and curl.
- Browser
- Download file 838 kB
-
https://huggingface.co/omarelsayeed/DISASTER_MODEL_PRECHATS/resolve/main/tokenizer.json
- Command line
-
hf download hf://omarelsayeed/DISASTER_MODEL_PRECHATS/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/omarelsayeed/DISASTER_MODEL_PRECHATS/resolve/main/tokenizer.json
838 kB
- Xet hash:
- f5c8a9f5ecacef1c13c445486909e7c22677897d3568121e3aaf3e0bf1100a17
- Size of remote file:
- 838 kB
- SHA256:
- cf64ea4b6f2156ee6ed9faf8cf10d83b17af75673821f7f418b619c046f2e25e
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