FRIDA is a full-scale finetuned general text embedding model inspired by denoising architecture based on T5. The model is based on the encoder part of
FRED-T5
model and continues research of text embedding models (
ruMTEB
,
ru-en-RoSBERTa
). It has been pre-trained on a Russian-English dataset and fine-tuned for improved performance on the target task.
For more model details please refer to our technical report [TODO].
Usage
The model can be used as is with prefixes. It is recommended to use CLS pooling. The choice of prefix and pooling depends on the task.
We use the following basic rules to choose a prefix:
"search_query: "
and
"search_document: "
prefixes are for answer or relevant paragraph retrieval
"paraphrase: "
prefix is for symmetric paraphrasing related tasks (STS, paraphrase mining, deduplication)
"categorize: "
prefix is for asymmetric matching of document title and body (e.g. news, scientific papers, social posts)
"categorize_sentiment: "
prefix is for any tasks that rely on sentiment features (e.g. hate, toxic, emotion)
"categorize_topic: "
prefix is intended for tasks where you need to group texts by topic
"categorize_entailment: "
prefix is for textual entailment task (NLI)
To better tailor the model to your needs, you can fine-tune it with relevant high-quality Russian and English datasets.
Below are examples of texts encoding using the Transformers and SentenceTransformers libraries.
Transformers
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, T5EncoderModel
defpool(hidden_state, mask, pooling_method="cls"):
if pooling_method == "mean":
s = torch.sum(hidden_state * mask.unsqueeze(-1).float(), dim=1)
d = mask.sum(axis=1, keepdim=True).float()
return s / d
elif pooling_method == "cls":
return hidden_state[:, 0]
inputs = [
# "paraphrase: В Ярославской области разрешили работу бань, но без посетителей",
"categorize_entailment: Женщину доставили в больницу, за ее жизнь сейчас борются врачи.",
"search_query: Сколько программистов нужно, чтобы вкрутить лампочку?",
# "paraphrase: Ярославским баням разрешили работать без посетителей",
"categorize_entailment: Женщину спасают врачи.",
"search_document: Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование."
]
tokenizer = AutoTokenizer.from_pretrained("ai-forever/FRIDA")
model = T5EncoderModel.from_pretrained("ai-forever/FRIDA")
tokenized_inputs = tokenizer(inputs, max_length=512, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
outputs = model(**tokenized_inputs)
embeddings = pool(
outputs.last_hidden_state,
tokenized_inputs["attention_mask"],
pooling_method="cls"# or try "mean"
)
embeddings = F.normalize(embeddings, p=2, dim=1)
sim_scores = embeddings[:3] @ embeddings[3:].T
print(sim_scores.diag().tolist())
# [0.9360030293464661, 0.8591322302818298, 0.728583037853241]
SentenceTransformers
from sentence_transformers import SentenceTransformer
inputs = [
# "paraphrase: В Ярославской области разрешили работу бань, но без посетителей",
"categorize_entailment: Женщину доставили в больницу, за ее жизнь сейчас борются врачи.",
"search_query: Сколько программистов нужно, чтобы вкрутить лампочку?",
# "paraphrase: Ярославским баням разрешили работать без посетителей",
"categorize_entailment: Женщину спасают врачи.",
"search_document: Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование."
]
# loads model with CLS pooling
model = SentenceTransformer("ai-forever/FRIDA")
# embeddings are normalized by default
embeddings = model.encode(inputs, convert_to_tensor=True)
sim_scores = embeddings[:3] @ embeddings[3:].T
print(sim_scores.diag().tolist())
# [0.9360026717185974, 0.8591331243515015, 0.7285830974578857]
or using prompts (sentence-transformers>=2.4.0):
from sentence_transformers import SentenceTransformer
# loads model with CLS pooling
model = SentenceTransformer("ai-forever/FRIDA")
paraphrase = model.encode(["В Ярославской области разрешили работу бань, но без посетителей", "Ярославским баням разрешили работать без посетителей"], prompt_name="paraphrase")
print(paraphrase[0] @ paraphrase[1].T) # 0.9360032
categorize_entailment = model.encode(["Женщину доставили в больницу, за ее жизнь сейчас борются врачи.", "Женщину спасают врачи."], prompt_name="categorize_entailment")
print(categorize_entailment[0] @ categorize_entailment[1].T) # 0.8591322
query_embedding = model.encode("Сколько программистов нужно, чтобы вкрутить лампочку?", prompt_name="search_query")
document_embedding = model.encode("Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование.", prompt_name="search_document")
print(query_embedding @ document_embedding.T) # 0.7285831
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ai-forever FRIDA online free url in huggingface.co:
FRIDA is an open source model from GitHub that offers a free installation service, and any user can find FRIDA on GitHub to install. At the same time, huggingface.co provides the effect of FRIDA install, users can directly use FRIDA installed effect in huggingface.co for debugging and trial. It also supports api for free installation.