remove redundant files

README.md

@@ -1,45 +0,0 @@
----
-library_name: light-embed
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-
----
-
-# sbert-all-MiniLM-L12-v2-onnx
-
-This is the ONNX version of the Sentence Transformers model sentence-transformers/all-MiniLM-L12-v2 for sentence embedding, optimized for speed and lightweight performance. By utilizing onnxruntime and tokenizers instead of heavier libraries like sentence-transformers and transformers, this version ensures a smaller library size and faster execution. Below are the details of the model:
-- Base model: sentence-transformers/all-MiniLM-L12-v2
-- Embedding dimension: 384
-- Max sequence length: 128
-- File size on disk:  0.12 GB
-
-This ONNX model consists all components in the original sentence transformer model:
-Transformer, Pooling, Normalize
-
-<!--- Describe your model here -->
-
-## Usage (LightEmbed)
-
-Using this model becomes easy when you have [LightEmbed](https://www.light-embed.net) installed:
-
-```
-pip install -U light-embed
-```
-
-Then you can use the model like this:
-
-```python
-from light_embed import TextEmbedding
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = TextEmbedding('sentence-transformers/all-MiniLM-L12-v2')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-## Citing & Authors
-
-Binh Nguyen / binhcode25@gmail.com

model_descriptions.json

@@ -1,6 +0,0 @@
-{
-  "base_model": "sentence-transformers/all-MiniLM-L12-v2",
-  "embedding_dim": 384,
-  "max_seq_length": 128,
-  "model_file_size (GB)": 0.12
-}