Update README.md

README.md

@@ -7,4 +7,71 @@ tags:
 - feature-extraction
 - text-embeddings-inference
 ---
-# Tarka Embedding 30M V1
+# Tarka Embedding 30M V1
+
+> [!NOTE]
+> ## Features
+> - Compressed model by 20x.
+> - Recovered approx. 86% performance on MTEB(Eng, v2) Benchmark
+
+For more details refer the [blog post](https://tarka-air.gitbook.io/home/tarka-v1/tarka-embedding-30m-v1)
+## Results
+
+### MTEB(Eng, V2)
+
+| Model                         | Parameters (B) | Mean (Task) | Mean (TaskType) | Classification | Clustering | Pair Classification | Reranking | Retrieval | STS   | Summarization |
+|------------------------------|----------------|-------------|------------------|----------------|------------|---------------------|-----------|-----------|-------|---------------|
+| all-MiniLM-L6-v2             | 0.023          | 59.03       | 55.93            | 69.25          | 44.9       | 82.37               | 47.14     | 42.92     | 78.95 | 25.96         |
+| gte-micro-v4                | 0.019          | 58.9        | 56.04            | 73.04          | 43.89      | 82.67               | 44.78     | 39.51     | 79.78 | 28.59         |
+| snowflake-arctic-embed-xs   | 0.023          | 59.77       | 56.12            | 67             | 42.44      | 81.33               | 45.26     | 52.65     | 76.21 | 27.96         |
+| gte-micro                   | 0.017          | 53.89       | 52.5             | 67.47          | 41.86      | 80.76               | 43.16     | 27.66     | 77.86 | 28.76         |
+| Qwen3 Embedding 0.6B        | 0.6            | 70.7        | 64.88            | 85.76          | 54.05      | 84.37               | 48.18     | 61.83     | 86.57 | 33.43         |
+| Tarka Embedding 30M V1 (S)  | 0.03           | 46.07       | 45.22            | 60.37          | 41.37      | 66.29               | 38.34     | 19.56     | 64.15 | 26.44         |
+| Tarka Embedding 30M V1 (M)  | 0.03           | 51.96       | 49.88            | 66.52          | 43.47      | 70.66               | 40.12     | 30.15     | 69.81 | 28.42         |
+| Tarka Embedding 30M V1 (L)  | 0.03           | 60.43       | 56.69            | 79.2           | 46.99      | 78.24               | 43.32     | 42.5      | 76.92 | 29.63         |
+
+
+
+## Usage
+```python
+from sentence_transformers import SentenceTransformer
+
+# We recommend enabling flash_attention_2 for better acceleration and memory saving,
+model = SentenceTransformer(
+    "Tarka-AIR/Tarka-Embedding-30M-V1",
+    trust_remote_code=True,
+    model_kwargs={
+        "attn_implementation": "flash_attention_2",
+        "device_map": "cuda",
+        "torch_dtype": "bfloat16",
+    },
+    tokenizer_kwargs={"padding_side": "left"},
+)
+
+# Config the model inference mode ("L","M","S")
+model[0].auto_model.configure_subnetwork("L")
+
+# The queries and documents to embed
+queries = [
+    "What is the capital of China?",
+    "Explain gravity",
+]
+documents = [
+    "The capital of China is Beijing.",
+    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
+]
+
+# Encode the queries and documents. Note that queries benefit from using a prompt
+# Here we use the prompt called "query" stored under `model.prompts`, but you can
+# also pass your own prompt via the `prompt` argument
+query_embeddings = model.encode(queries, prompt_name="query")
+document_embeddings = model.encode(documents)
+
+# Compute the (cosine) similarity between the query and document embeddings
+similarity = model.similarity(query_embeddings, document_embeddings)
+print(similarity)
+
+# tensor([[0.8371, 0.1740],
+#         [0.2176, 0.6293]])
+
+```