Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+pipeline_tag: sentence-similarity
+
+---
+
+# SLX-v0.1
+
+**SLX-v0.1** is an advanced model developed by [BRAHMAI Research](https://brahmai.in), specifically designed for mapping sentences and paragraphs into a 384-dimensional dense vector space. This model is ideal for tasks such as clustering, semantic search, and sentence similarity.
+
+## Usage with Sentence-Transformers
+
+Using SLX-v0.1 is straightforward with the [Sentence-Transformers](https://www.SBERT.net) library. Follow the instructions below to get started:
+
+### Installation
+
+```bash
+pip install -U sentence-transformers
+```
+
+### Example
+
+```python
+from sentence_transformers import SentenceTransformer
+
+sentences = ["This is an example sentence", "Each sentence is converted"]
+model = SentenceTransformer('brahmairesearch/slx-v0.1')
+embeddings = model.encode(sentences)
+
+print(embeddings)
+```
+
+## Usage with Hugging Face Transformers
+
+If you prefer to use Hugging Face Transformers without the Sentence-Transformers library, you can still utilize the SLX-v0.1 model. Below is a guide on how to process input and apply mean pooling to obtain sentence embeddings:
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
+
+# Mean Pooling - Takes attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]  # Token embeddings from the model output
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+# Sentences for embedding
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained('brahmairesearch/slx-v0.1')
+model = AutoModel.from_pretrained('brahmairesearch/slx-v0.1')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+# Normalize embeddings
+sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+## Evaluation Results
+
+For an automated evaluation of SLX-v0.1, please refer to the [Sentence Embeddings Benchmark](https://seb.sbert.net?model_name=brahmairesearch/slx-v0.1).
+
+## Intended Use Cases
+
+SLX-v0.1 is designed for encoding sentences and short paragraphs into dense vectors that capture their semantic information. These embeddings can be used for:
+
+- Information retrieval
+- Clustering
+- Sentence similarity tasks
+
+Please note that input text longer than 256 word pieces will be truncated by default.
+
+## Training Procedure
+
+### Pre-training
+
+SLX-v0.1 is based on the pre-trained model [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased). For more information on the pre-training process, please refer to the original model card.
+
+### Fine-tuning
+
+The model was fine-tuned using a contrastive learning objective. Cosine similarity is computed between all possible sentence pairs within a batch, and cross-entropy loss is applied using the true pairs. 
+
+Following fine-tuning, the model underwent transfer learning using the [`dunzhang/stella_en_400M_v5`](https://huggingface.co/dunzhang/stella_en_400M_v5) model with an internally curated dataset, optimizing it for English language tasks.
+
+## Contact
+
+For inquiries or feedback, feel free to reach out to us at [email protected].
+
+Best regards,  
+The [BRAHMAI](https://brahmai.in) Team