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--- |
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license: mit |
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--- |
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# intel-optimized-model-for-embeddings-v1 |
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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). |
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## Usage |
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Install the required packages: |
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``` |
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pip install -U torch==2.3.1+cpu --extra-index-url https://download.pytorch.org/whl/cpu |
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pip install -U transformers==4.42.4 intel-extension-for-pytorch==2.3.100 |
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``` |
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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. |
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``` |
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import torch |
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from transformers import AutoTokenizer, AutoModel |
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import intel_extension_for_pytorch as ipex |
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def mean_pooling(model_output, attention_mask): |
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token_embeddings = model_output[0] |
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
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return torch.sum(token_embeddings * input_mask_expanded, |
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1) / torch.clamp(input_mask_expanded.sum(1), |
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min=1e-9) |
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# load model |
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tokenizer = AutoTokenizer.from_pretrained('Intel/intel-optimized-model-for-embeddings-v1') |
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model = AutoModel.from_pretrained('Intel/intel-optimized-model-for-embeddings-v1', |
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torchscript=True) |
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model.eval() |
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# do IPEX optimization |
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batch_size = 1 |
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seq_length=512 |
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vocab_size = model.config.vocab_size |
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sample_input = {"input_ids": torch.randint(vocab_size, size=[batch_size, seq_length]), |
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"token_type_ids": torch.zeros(size=[batch_size, seq_length], |
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dtype=torch.int), |
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"attention_mask": torch.randint(1, size=[batch_size, seq_length])} |
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text = "This is a test." |
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model = ipex.optimize(model, level="O1",auto_kernel_selection=True, |
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conv_bn_folding=False, dtype=torch.bfloat16) |
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with torch.no_grad(), torch.cpu.amp.autocast(cache_enabled=False, |
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dtype=torch.bfloat16): |
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# Compile model |
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model = torch.jit.trace(model, example_kwarg_inputs=sample_input, |
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check_trace=False, strict=False) |
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model = torch.jit.freeze(model) |
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# Call model |
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tokenized_text = tokenizer(text, padding=True, truncation=True, return_tensors='pt') |
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model_output = model(**tokenized_text) |
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sentence_embeddings = mean_pooling(model_output,tokenized_text['attention_mask']) |
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embeddings = sentence_embeddings[0].tolist() |
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# Embeddings output |
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print(embeddings) |
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``` |
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## Model Details |
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### Model Description |
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This model was fine-tuned using the [sentence-transformers](https://github.com/UKPLab/sentence-transformers) library |
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based on the [BERT-Medium_L-8_H-512_A-8](https://huggingface.co/nreimers/BERT-Medium_L-8_H-512_A-8) model |
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using [UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1) as a teacher. |
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### Training Datasets |
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| Dataset | Description | License | |
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| ------------- |:-------------:| -----:| |
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| beir/dbpedia-entity | DBpedia-Entity is a standard test collection for entity search over the DBpedia knowledge base. | CC BY-SA 3.0 license | |
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| 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 | |
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| beir/scidocs | SciDocs is a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. | CC-BY-SA-4.0 license | |
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| 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 | |
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| 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 | |
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| WikiAnswers | The WikiAnswers corpus contains clusters of questions tagged by WikiAnswers users as paraphrases. | MIT | |
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| 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 | |
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| 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 | |
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