Feature Extraction
sentence-transformers
Safetensors
Transformers
mistral
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use BAAI/bge-en-icl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-en-icl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-en-icl") 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 BAAI/bge-en-icl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/bge-en-icl")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-en-icl") model = AutoModel.from_pretrained("BAAI/bge-en-icl", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files
README.md
CHANGED
|
@@ -1793,9 +1793,9 @@ def get_new_queries(queries, query_max_len, examples_prefix, tokenizer):
|
|
| 1793 |
return_tensors=None,
|
| 1794 |
add_special_tokens=False
|
| 1795 |
)
|
| 1796 |
-
prefix_ids = tokenizer(examples_prefix)['input_ids']
|
| 1797 |
-
suffix_ids = tokenizer('\n<response>')['input_ids']
|
| 1798 |
-
new_max_length = (len(prefix_ids) + len(suffix_ids) + query_max_len) // 8 * 8 + 8
|
| 1799 |
new_queries = tokenizer.batch_decode(inputs['input_ids'])
|
| 1800 |
for i in range(len(new_queries)):
|
| 1801 |
new_queries[i] = examples_prefix + new_queries[i] + '\n<response>'
|
|
|
|
| 1793 |
return_tensors=None,
|
| 1794 |
add_special_tokens=False
|
| 1795 |
)
|
| 1796 |
+
prefix_ids = tokenizer(examples_prefix, add_special_tokens=False)['input_ids']
|
| 1797 |
+
suffix_ids = tokenizer('\n<response>', add_special_tokens=False)['input_ids']
|
| 1798 |
+
new_max_length = (len(prefix_ids) + len(suffix_ids) + query_max_len + 8) // 8 * 8 + 8
|
| 1799 |
new_queries = tokenizer.batch_decode(inputs['input_ids'])
|
| 1800 |
for i in range(len(new_queries)):
|
| 1801 |
new_queries[i] = examples_prefix + new_queries[i] + '\n<response>'
|