Sentence Similarity
Transformers
Safetensors
English
mistral
feature-extraction
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
text-reranking
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
custom_code
text-generation-inference
text-embeddings-inference
Inference Endpoints
Update README.md
Browse files
README.md
CHANGED
|
@@ -29,7 +29,7 @@ tags:
|
|
| 29 |
|
| 30 |
> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.
|
| 31 |
- **Repository:** https://github.com/McGill-NLP/llm2vec
|
| 32 |
-
|
| 33 |
|
| 34 |
## Installation
|
| 35 |
```bash
|
|
|
|
| 29 |
|
| 30 |
> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.
|
| 31 |
- **Repository:** https://github.com/McGill-NLP/llm2vec
|
| 32 |
+
- **Paper:** https://arxiv.org/abs/2404.05961
|
| 33 |
|
| 34 |
## Installation
|
| 35 |
```bash
|