đ econosentence
A sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space, suitable for tasks like clustering or semantic search, especially in NLP applied to economics.
Key Information
Property |
Details |
Pipeline Tag |
sentence-similarity |
Tags |
sentence-transformers, feature-extraction, sentence-similarity, transformers |
Datasets |
samchain/econo-pairs |
Language |
en |
Metrics |
pearsonr |
Library Name |
sentence-transformers |
License |
apache-2.0 |
đ Quick Start
Econosentence can be used for various NLP tasks in economics, with the main application being topic modeling using embeddings. The encoder backbone is the model samchain/EconoBert, pretrained with MLM and NSP tasks on the samchain/BIS_Speeches_97_23.
đĻ Installation
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
đģ Usage Examples
Basic Usage
If you have sentence-transformers installed:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('econo-sentence')
embeddings = model.encode(sentences)
print(embeddings)
Advanced Usage
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
from transformers import AutoTokenizer, AutoModel
import torch
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
sentences = ['This is an example sentence', 'Each sentence is converted']
tokenizer = AutoTokenizer.from_pretrained('econo-sentence')
model = AutoModel.from_pretrained('econo-sentence')
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
đ Documentation
Evaluation Results
The Pearson correlation for the train test is : 0.83.
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
Training
The model was trained with the following parameters:
DataLoader:
torch.utils.data.dataloader.DataLoader
of length 2237 with parameters:
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
Loss:
sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss
Parameters of the fit()-Method:
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
đ License
This project is licensed under the apache-2.0 license.
đ Citing & Authors
Samuel Chaineau