init minicoil

README.md

@@ -1,37 +1,14 @@
 ---
-base_model: jinaai/jina-embeddings-v2-small-en
-library_name: transformers.js
-pipeline_tag: feature-extraction
 ---
 
-https://huggingface.co/jinaai/jina-embeddings-v2-small-en with ONNX weights to be compatible with Transformers.js.
+# MiniCOIL v1
 
-## Usage with 🤗 Transformers.js
+MiniCOIL - is a sparse contextualized per-token embeddings.
+Read more about it in [the article](https://qdrant.tech/articles/minicoil).
 
-If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
-```bash
-npm i @huggingface/transformers
-```
 
-You can then use the model as follows:
-```js
-import { pipeline, cos_sim } from '@huggingface/transformers';
+## Usage
 
-// Create feature extraction pipeline
-const extractor = await pipeline('feature-extraction', 'Xenova/jina-embeddings-v2-small-en',
-    { dtype: "fp32" } // Options: "fp32", "fp16", "q8", "q4"
-);
+This model is designed to be used with [FastEmbed](https://github.com/qdrant/fastembed) library.
 
-// Generate embeddings
-const output = await extractor(
-    ['How is the weather today?', 'What is the current weather like today?'],
-    { pooling: 'mean' }
-);
-
-// Compute cosine similarity
-console.log(cos_sim(output[0].data, output[1].data));  // 0.9399812684139274 (unquantized) vs. 0.9341121503699659 (quantized)
-```
-
----
-
-Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
+ToDo

stopwords.txt

@@ -0,0 +1,179 @@
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