README.md

# ember-v1

<p align="center">
<img src="https://console.llmrails.com/assets/img/logo-black.svg" width="150px">
</p>

This model is trained on a large-scale corpus of relevance text pairs, covering a wide range of domains like financial, scientific, medical, legal  and others. While training we used some technics from Retromae and SetFit papers.

We are also providing it on our own platform as API as a service, feel free to signup: [LLMRails](https://llmrails.com/?ref=ember-v1).

### Plans
- Paper will be published soon
- v2 is on it's way with 4k maximum sequence length

## Usage
Use with API request:
```bash
curl --location 'https://api.llmrails.com/v1/embeddings' \
--header 'X-API-KEY: {token}' \
--header 'Content-Type: application/json' \
--data '{
   "input": ["This is an example sentence"],
   "model":"embedding-english-v1" # equals to ember-v1
}'
```
API docs: https://docs.llmrails.com/embedding/embed-text
Langchain plugin: https://python.langchain.com/docs/integrations/text_embedding/llm_rails

Use with transformers:
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel

def average_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]

input_texts = [
    "This is an example sentence",
    "Each sentence is converted"
]

tokenizer = AutoTokenizer.from_pretrained("llmrails/ember-v1")
model = AutoModel.from_pretrained("llmrails/ember-v1")

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
```

Use with sentence-transformers:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

sentences = [
    "This is an example sentence",
    "Each sentence is converted"
]

model = SentenceTransformer('llmrails/ember-v1')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```

## Massive Text Embedding Benchmark (MTEB) Evaluation
Our model achieve state-of-the-art performance on [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard)

|                               Model Name                                | Dimension | Sequence Length | Average (56) | 
|:-----------------------------------------------------------------------:|:---------:|:---:|:------------:|
| [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) |   1024    |       512       |    64.23     |  
| [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) |   768    |       512       |    63.55     |
| [ember-v1](https://huggingface.co/llmrails/emmbedding-en-v1) |   1024    | 512 |    **63.54**     |  
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings/types-of-embedding-models) |   1536    |      8191       |    60.99     |

### Limitation

This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.