pipeline_tag: sentence-similarity
tags:
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
mutual information Contrastive Sentence Embedding (miCSE):
Language model of the pre-print arXiv paper titled: "miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings"
Brief Model Description
The miCSE language model is trained for sentence similarity computation. Training the model imposes alignment between the attention pattern of different views (embeddings of augmentations) during contrastive learning. Intuitively, learning sentence embeddings with miCSE entails enforcing syntactic consistency across dropout augmented views. Practically, this is achieved by regularizing the self-attention distribution. By regularizing self-attention during training, representation learning becomes much more sample efficient. Hence, self-supervised learning becomes tractable even when the training set is limited in size. This property makes miCSE particularly interesting for real-world applications, where training data is typically limited.
Model Use Cases
The model intended to be used for encoding sentences or short paragraphs. Given an input text, the model produces a vector embedding capturing the semantics. Sentence representations correspond to embedding of the [CLS] token. The embedding can be used for numerous tasks such as retrieval,sentence similarity comparison (see example 1) or clustering (see example 2).
Training data
The model was trained on a random collection of English sentences from Wikipedia: Training data file
Model Usage
Example 1) - Sentence Similarity
from transformers import AutoTokenizer, AutoModel
import torch.nn as nn
tokenizer = AutoTokenizer.from_pretrained("sap-ai-research/miCSE")
model = AutoModel.from_pretrained("sap-ai-research/miCSE")
# Encoding of sentences in a list with a predefined maximum lengths of tokens (max_length)
max_length = 32
sentences = [
"This is a sentence for testing miCSE.",
"This is yet another test sentence for the mutual information Contrastive Sentence Embeddings model."
]
batch = tokenizer.batch_encode_plus(
sentences,
return_tensors='pt',
padding=True,
max_length=max_length,
truncation=True
)
# Compute the embeddings and keep only the _**[CLS]**_ embedding (the first token)
# Get raw embeddings (no gradients)
with torch.no_grad():
outputs = model(**batch, output_hidden_states=True, return_dict=True)
embeddings = outputs.last_hidden_state[:,0]
# Define similarity metric, e.g., cosine similarity
sim = nn.CosineSimilarity(dim=-1)
# Compute similarity between the **first** and the **second** sentence
cos_sim = sim(embeddings.unsqueeze(1),
embeddings.unsqueeze(0))
print(f"Distance: {cos_sim[0,1].detach().item()}")
Example 2) - Clustering
from transformers import AutoTokenizer, AutoModel
import torch.nn as nn
import torch
import numpy as np
import tqdm
from datasets import load_dataset
import umap
import umap.plot as umap_plot
# Determine available hardware
if torch.backends.mps.is_available():
device = torch.device("mps")
elif torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("/Users/d065243/miCSE")
model = AutoModel.from_pretrained("/Users/d065243/miCSE")
model.to(device);
# Load Twitter data for sentiment clustering
dataset = load_dataset("tweet_eval", "sentiment")
# Compute embeddings of the tweets
# set batch size and maxium tweet token length
batch_size = 50
max_length = 128
iterations = int(np.floor(len(dataset['train'])/batch_size))*batch_size
embedding_stack = []
classes = []
for i in tqdm.notebook.tqdm(range(0,iterations,batch_size)):
# create batch
batch = tokenizer.batch_encode_plus(
dataset['train'][i:i+batch_size]['text'],
return_tensors='pt',
padding=True,
max_length=max_length,
truncation=True
).to(device)
classes = classes + dataset['train'][i:i+batch_size]['label']
# model inference without gradient
with torch.no_grad():
outputs = model(**batch, output_hidden_states=True, return_dict=True)
embeddings = outputs.last_hidden_state[:,0]
embedding_stack.append( embeddings.cpu().clone() )
embeddings = torch.vstack(embedding_stack)
# Cluster embeddings in 2D with UMAP
umap_model = umap.UMAP(n_neighbors=250,
n_components=2,
min_dist=1.0e-9,
low_memory=True,
angular_rp_forest=True,
metric='cosine')
umap_model.fit(embeddings)
# Plot result
umap_plot.points(umap_model, labels = np.array(classes),theme='fire')
Example 3) - Using SentenceTransformers
from sentence_transformers import SentenceTransformer, util
from sentence_transformers import models
import torch.nn as nn
# Using the model with [CLS] embeddings
model_name = 'sap-ai-research/miCSE'
word_embedding_model = models.Transformer(model_name, max_seq_length=32)
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())
model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
# Using cosine similarity as metric
cos_sim = nn.CosineSimilarity(dim=-1)
# List of sentences for comparison
sentences_1 = ["This is a sentence for testing miCSE.",
"This is using mutual information Contrastive Sentence Embeddings model."]
sentences_2 = ["This is testing miCSE.",
"Similarity with miCSE"]
# Compute embedding for both lists
embeddings_1 = model.encode(sentences_1, convert_to_tensor=True)
embeddings_2 = model.encode(sentences_2, convert_to_tensor=True)
# Compute cosine similarities
cosine_sim_scores = cos_sim(embeddings_1, embeddings_2)
#Output of results
for i in range(len(sentences1)):
print(f"Similarity {cosine_scores[i][i]:.2f}: {sentences1[i]} << vs. >> {sentences2[i]}")
Benchmark
Model results on SentEval Benchmark:
+-------+-------+-------+-------+-------+--------------+-----------------+--------+
| STS12 | STS13 | STS14 | STS15 | STS16 | STSBenchmark | SICKRelatedness | S.Avg. |
+-------+-------+-------+-------+-------+--------------+-----------------+--------+
| 71.71 | 83.09 | 75.46 | 83.13 | 80.22 | 79.70 | 73.62 | 78.13 |
+-------+-------+-------+-------+-------+--------------+-----------------+--------+
Citations
If you use this code in your research or want to refer to our work, please cite:
@article{Klein2022miCSEMI,
title={miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings},
author={Tassilo Klein and Moin Nabi},
journal={ArXiv},
year={2022},
volume={abs/2211.04928}
}