--- tags: - mteb - transformers model-index: - name: Zeta-Alpha-E5-Mistral results: - dataset: config: en-ext name: MTEB AmazonCounterfactualClassification (en-ext) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 78.61319340329835 - type: ap value: 26.407069359264725 - type: ap_weighted value: 26.407069359264725 - type: f1 value: 65.36726648428174 - type: f1_weighted value: 82.25843407936554 - type: main_score value: 78.61319340329835 task: type: Classification - dataset: config: en name: MTEB AmazonCounterfactualClassification (en) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 77.76119402985074 - type: ap value: 39.97673988468886 - type: ap_weighted value: 39.97673988468886 - type: f1 value: 71.23171737695898 - type: f1_weighted value: 79.55230307558237 - type: main_score value: 77.76119402985074 task: type: Classification - 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type: accuracy value: 88.63920844012637 - type: accuracy_threshold value: 84.5294713973999 - type: ap value: 81.41443623323144 - type: f1 value: 74.84396892115653 - type: f1_threshold value: 82.87262320518494 - type: precision value: 72.34671263235656 - type: recall value: 77.5197889182058 - type: main_score value: 81.41444048232692 task: type: PairClassification - dataset: config: default name: MTEB TwitterURLCorpus (default) revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf split: test type: mteb/twitterurlcorpus-pairclassification metrics: - type: accuracy value: 89.90569332867622 - type: accuracy_threshold value: 81.01733326911926 - type: ap value: 87.91824445175028 - type: f1 value: 80.35081439054949 - type: f1_threshold value: 78.65387201309204 - type: precision value: 75.0853013982739 - type: recall value: 86.41053279950724 - type: main_score value: 87.91825594329686 task: type: PairClassification language: - en license: mit --- ## Zeta-Alpha-E5-Mistral We introduce Zeta Alpha's first public embedding model, a retrieval-specialized, 7B parameter embedding model trained on top of [E5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct). This model marks the first published model from Zeta Alpha's open science embedding models. The full dataset and detailed training recipe will be made available (very) soon. We will also make available our evaluation dataset (Called `Mini` and `Nano` versions of `BEIR`), which allowed us to quickly evaluate the model's performance while training. ### Lora Weights The lora weights are also available, so there is no need to download the full model. ## How to Run The model was trained with the same instruction-tuning strategy as the original [E5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) model. Therefore, queries should be formatted as follows: ``` Instruct: \nQuery: ``` ### Transformers ``` python import torch import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) if left_padding: return last_hidden_states[:, -1] else: sequence_lengths = attention_mask.sum(dim=1) - 1 batch_size = last_hidden_states.shape[0] return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths] def get_detailed_instruct(task_description: str, query: str) -> str: return f'Instruct: {task_description}\nQuery: {query}' task = "Given a claim about climate change, retrieve documents that support or refute the claim" queries = [ get_detailed_instruct(task, "In Alaska, brown bears are changing their feeding habits to eat elderberries that ripen earlier."), get_detailed_instruct(task, "Local and regional sea levels continue to exhibit typical natural variability—in some places rising and in others falling.") ] passages = [ "The brown bear ( Ursus arctos ) is a large bear with the widest distribution of any living ursid . The species is distributed across much of northern Eurasia and North America . It is one of the two largest terrestrial carnivorans alive today , rivaled in body size only by its close cousin , the polar bear ( Ursus maritimus ) , which is much less variable in size and averages larger due to this . There are several recognized subspecies , many of which are quite well-known within their native ranges , found in the brown bear species . The brown bear 's principal range includes parts of Russia , Central Asia , China , Canada , the United States ( mostly Alaska ) , Scandinavia and the Carpathian region ( especially Romania ) , Anatolia , and Caucasus . The brown bear is recognized as a national and state animal in several European countries . While the brown bear 's range has shrunk and it has faced local extinctions , it remains listed as a least concern species by the International Union for Conservation of Nature ( IUCN ) with a total population of approximately 200,000 . As of 2012 , this and the American black bear are the only bear species not classified as threatened by the IUCN . However , the Californian , North African ( Atlas bear ) , and Mexican subspecies were hunted to extinction in the nineteenth and early twentieth centuries , and many of the southern Asian subspecies are highly endangered . One of the smaller-bodied subspecies , the Himalayan brown bear , is critically endangered , occupying only 2 % of its former range and threatened by uncontrolled poaching for its parts . The Marsican brown bear , one of several currently isolated populations of the main Eurasian brown bear race , in central Italy is believed to have a population of just 30 to 40 bears .", "ean sea level ( MSL ) ( abbreviated simply sea level ) is an average level of the surface of one or more of Earth 's oceans from which heights such as elevations may be measured . MSL is a type of vertical datuma standardised geodetic reference pointthat is used , for example , as a chart datum in cartography and marine navigation , or , in aviation , as the standard sea level at which atmospheric pressure is measured in order to calibrate altitude and , consequently , aircraft flight levels . A common and relatively straightforward mean sea-level standard is the midpoint between a mean low and mean high tide at a particular location . Sea levels can be affected by many factors and are known to have varied greatly over geological time scales . The careful measurement of variations in MSL can offer insights into ongoing climate change , and sea level rise has been widely quoted as evidence of ongoing global warming . The term above sea level generally refers to above mean sea level ( AMSL ) ." ] # load model and tokenizer tokenizer = AutoTokenizer.from_pretrained("zeta-alpha-ai/Zeta-Alpha-E5-Mistral") model = AutoModel.from_pretrained("zeta-alpha-ai/Zeta-Alpha-E5-Mistral") # get the embeddings max_length = 4096 input_texts = queries + passages batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors="pt") outputs = model(**batch_dict) embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) [[66.15530395507812, 43.65541458129883], [47.681705474853516, 63.67986297607422]] ``` ### Zeta Alpha Zeta Alpha is the premier Neural Discovery Platform for AI and more. We are an Amsterdam-based R&D and product lab with a passion for AI technology, with offices on the Science Park campus of the University of Amsterdam. and in San Francisco. The Zeta Alpha Research team: - Arthur Câmara - Dinos Papakostas - Mathias Parisot - Fernando Rejon Barrera - Jakub Zavrel