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import spaces |
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import torch |
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import torch.nn.functional as F |
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from torch import Tensor |
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from transformers import AutoTokenizer, AutoModel |
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import gradio as gr |
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import os |
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title = """ |
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# 👋🏻Welcome to 🙋🏻♂️Tonic's 🐣e5-mistral🛌🏻Embeddings """ |
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description = """ |
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You can use this Space to test out the current model [intfloat/e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct). e5mistral has a larger context window, a different prompting/return mechanism and generally better results than other embedding models. |
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You can also use 🐣e5-mistral🛌🏻 by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/e5?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> |
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Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [Poly](https://github.com/tonic-ai/poly) |
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""" |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-mistral-7b-instruct') |
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model = AutoModel.from_pretrained('intfloat/e5-mistral-7b-instruct', torch_dtype=torch.float16, device_map=device) |
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def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: |
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left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) |
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if left_padding: |
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return last_hidden_states[:, -1] |
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else: |
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sequence_lengths = attention_mask.sum(dim=1) - 1 |
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batch_size = last_hidden_states.shape[0] |
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return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths] |
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def get_detailed_instruct(task_description: str, query: str) -> str: |
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return f'Instruct: {task_description}\nQuery: {query}' |
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@spaces.GPU |
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def compute_embeddings(*input_texts): |
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max_length = 2042 |
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task = 'Given a web search query, retrieve relevant passages that answer the query' |
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processed_texts = [get_detailed_instruct(task, text) for text in input_texts] |
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batch_dict = tokenizer(processed_texts, max_length=max_length - 1, return_attention_mask=False, padding=False, truncation=True) |
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batch_dict['input_ids'] = [input_ids + [tokenizer.eos_token_id] for input_ids in batch_dict['input_ids']] |
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batch_dict = tokenizer.pad(batch_dict, padding=True, return_attention_mask=True, return_tensors='pt') |
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batch_dict = {k: v.to(device) for k, v in batch_dict.items()} |
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outputs = model(**batch_dict) |
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embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask']) |
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embeddings = F.normalize(embeddings, p=2, dim=1) |
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embeddings_list = embeddings.detach().cpu().numpy().tolist() |
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return embeddings_list |
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def app_interface(): |
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with gr.Blocks() as demo: |
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gr.Markdown(title) |
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gr.Markdown(description) |
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input_text_boxes = [gr.Textbox(label=f"Input Text {i+1}") for i in range(4)] |
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compute_button = gr.Button("Compute Embeddings") |
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output_display = gr.Dataframe(headers=["Embedding Value"], datatype=["number"]) |
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with gr.Row(): |
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with gr.Column(): |
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for text_box in input_text_boxes: |
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text_box |
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with gr.Column(): |
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compute_button |
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output_display |
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compute_button.click( |
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fn=compute_embeddings, |
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inputs=input_text_boxes, |
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outputs=output_display |
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) |
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return demo |
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app_interface().launch() |
