Text Classification
sentence-transformers
Safetensors
Transformers
qwen3
text-generation
sentence-similarity
feature-extraction
text-embeddings-inference
Instructions to use yourleige/test_model_upload with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yourleige/test_model_upload with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yourleige/test_model_upload") 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 yourleige/test_model_upload with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yourleige/test_model_upload")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("yourleige/test_model_upload") model = AutoModelForCausalLM.from_pretrained("yourleige/test_model_upload", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": true, | |
| "include_prompt": true | |
| } |