Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+---
+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
+---
+
+# intel-optimized-model-for-embeddings-v1
+
+This is a text embedding model model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. For sample code that uses this model in a torch serve container see [Intel-Optimized-Container-for-Embeddings](https://github.com/intel/Intel-Optimized-Container-for-Embeddings).
+
+## Usage
+
+Install the required packages:
+```
+pip install -U torch==2.3.1+cpu --extra-index-url https://download.pytorch.org/whl/cpu
+pip install -U transformers==4.42.4 intel-extension-for-pytorch==2.3.100
+```
+
+Use the following example below to load the model with the transformers library, tokenize the text, run the model, and apply pooling to the output.
+
+```
+# example embedding code
+import torch
+from transformers import AutoTokenizer, AutoModel
+import intel_extension_for_pytorch as ipex
+
+# load model
+tokenizer = AutoTokenizer.from_pretrained('Intel/intel-optimized-model-for-embeddings-v1')
+model = AutoModel.from_pretrained('Intel/intel-optimized-model-for-embeddings-v1', torchscript=True)
+model.eval()
+
+# do IPEX optimization
+batch_size = 1
+seq_length=512
+vocab_size = model.config.vocab_size
+sample_input = {"input_ids": torch.randint(vocab_size, size=[batch_size, seq_length]),
+                "token_type_ids": torch.zeros(size=[batch_size, seq_length],
+                                                dtype=torch.int),
+                "attention_mask": torch.randint(1, size=[batch_size, seq_length])}
+text = "This is a test."
+model = ipex.optimize(model, level="O1",auto_kernel_selection=True,
+                            conv_bn_folding=False, dtype=torch.bfloat16)
+
+with torch.no_grad(), torch.cpu.amp.autocast(cache_enabled=False,
+                                                dtype=torch.bfloat16):
+    # Compile model
+    model = torch.jit.trace(model, example_kwarg_inputs=sample_input,
+                                    check_trace=False, strict=False)
+    model = torch.jit.freeze(model)
+    
+    # Call model
+    tokenized_text = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
+    model_output = model(**tokenized_text)
+
+    # Do mean pooling
+    token_embeddings = model_output[0]
+    attention_mask = tokenized_text['attention_mask']
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    output_sum = torch.sum(token_embeddings * input_mask_expanded, 1)
+    embeddings = output_sum / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+    embeddings = [embeddings[0].tolist()]
+
+# Embeddings output
+print(embeddings)
+```
+
+## Model Details
+
+### Model Description
+
+This model was fine-tuned using the [sentence-transformers](https://github.com/UKPLab/sentence-transformers) library based on the [BERT-Medium_L-8_H-512_A-8](https://huggingface.co/nreimers/BERT-Medium_L-8_H-512_A-8) model.
+
+
+### Training Datasets
+
+| Dataset       | Description           | License  |
+| ------------- |:-------------:| -----:|
+| beir/dbpedia-entity      | DBpedia-Entity is a standard test collection for entity search over the DBpedia knowledge base.  | CC BY-SA 3.0 license |
+| beir/nq      | To help spur development in open-domain question answering, the Natural Questions (NQ) corpus has been created, along with a challenge website based on this data.       |   CC BY-SA 3.0 license |
+| beir/scidocs | SciDocs is a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation.       |    GNU General Public License v3.0 license  |
+| beir/trec-covid | TREC-COVID followed the TREC model for building IR test collections through community evaluations of search systems.       |  CC-BY-SA-4.0 license  |
+| beir/touche2020 | Given a question on a controversial topic, retrieve relevant arguments from a focused crawl of online debate portals.      |    CC BY 4.0 license  |
+| WikiAnswers | The WikiAnswers corpus contains clusters of questions tagged by WikiAnswers users as paraphrases.       |    MIT |
+| Cohere/wikipedia-22-12-en-embeddings Dataset  | The Cohere/Wikipedia dataset is a processed version of the wikipedia-22-12 dataset. It is English only, and the articles are broken up into paragraphs.       |    Apache 2.0  |
+| MLNI  | GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.       |    MIT |