Text Classification
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
sentence-similarity
text-embeddings-inference
Instructions to use randypang/intent-simple-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use randypang/intent-simple-chat with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("randypang/intent-simple-chat") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use randypang/intent-simple-chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="randypang/intent-simple-chat")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("randypang/intent-simple-chat") model = AutoModel.from_pretrained("randypang/intent-simple-chat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 649 Bytes
7246919 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": true,
"mask_token": "[MASK]",
"model_max_length": 1000000000000000019884624838656,
"name_or_path": "/root/.cache/torch/sentence_transformers/firqaaa_indo-sentence-bert-base/",
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": "/root/.cache/huggingface/transformers/b515a756d9ddf12a7a391ea596c488ac805f0576790934e590ce250a3e4ff056.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
|