Feature Extraction
sentence-transformers
Safetensors
English
colqwen2
multi-vector
colbert
late-interaction
Generated from Trainer
dataset_size:3475
loss:MultiVectorMultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use tomaarsen/multivector-colqwen2-v1.0-hf-docqa-energy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/multivector-colqwen2-v1.0-hf-docqa-energy with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/multivector-colqwen2-v1.0-hf-docqa-energy") 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] - Notebooks
- Google Colab
- Kaggle
| {% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system | |
| You are a helpful assistant.<|im_end|> | |
| {% endif %}<|im_start|>{{ message['role'] }} | |
| {% if message['content'] is string %}{{ message['content'] }}<|im_end|> | |
| {% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|> | |
| {% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant | |
| {% endif %} |