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") - Notebooks
- Google Colab
- Kaggle
| { | |
| "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]" | |
| } | |