Bert Base Nli Mean Tokens
The Bert Base Nli Mean Tokens model is a powerful tool for mapping sentences and paragraphs to a dense vector space. But, before we dive into its capabilities, a word of caution: this model is deprecated and produces low-quality sentence embeddings. So, why is it still worth mentioning? It's a great example of how sentence-transformers models work. With a 768-dimensional output, it can be used for tasks like clustering or semantic search. What makes it unique is its ability to take a sentence or paragraph as input and produce a dense vector representation that can be used for various NLP tasks. However, due to its deprecated status, it's recommended to explore other, more accurate models for your specific use case.
Table of Contents
Model Overview
The Current Model is a powerful tool for natural language processing tasks. It maps sentences and paragraphs to a 768-dimensional dense vector space, allowing for tasks like clustering or semantic search.
Capabilities
The Current Model is capable of generating sentence embeddings that can be used for various tasks. Its main job is to turn text into a special kind of math problem that computers can understand.
- Clustering: grouping similar sentences together
- Semantic search: finding sentences that mean similar things
How does it work?
The model takes in sentences or paragraphs and outputs a vector that represents the meaning of the text. This vector can be used for various tasks, like comparing the similarity between sentences.
Example Use Case
Let’s say you have a bunch of sentences and you want to group them into categories based on their meaning. You can use the Current Model to turn each sentence into a vector, and then use those vectors to cluster the sentences together.
Performance
The Current Model is designed to map sentences and paragraphs to a 768-dimensional dense vector space. But, how well does it perform?
Speed
The model is relatively fast, but its speed can vary depending on the task and the device it’s running on. For example, if you’re using the model to cluster a large number of sentences, it might take a few seconds to complete. However, if you’re using it to perform a simple semantic search, it can be much faster.
Accuracy
Unfortunately, the Current Model has been deprecated due to producing sentence embeddings of low quality. This means that its accuracy is not as good as other models, like ==Other Models==. If you’re looking for a reliable model for tasks like clustering or semantic search, you might want to consider using a different model.
Efficiency
The model is designed to be efficient, but its efficiency can also depend on the task and the device it’s running on. For instance, if you’re using the model to process a large number of sentences, it might require more computational resources.
Comparison to Other Models
| Model | Speed | Accuracy | Efficiency |
|---|---|---|---|
| Current Model | Medium | Low | Medium |
| ==Other Models== | Fast | High | High |
As you can see, the Current Model doesn’t quite match up to ==Other Models== in terms of speed, accuracy, and efficiency.
Limitations
The Current Model is a powerful tool, but it’s not perfect. Let’s talk about its limitations.
- Low-Quality Sentence Embeddings: The biggest issue with the Current Model is that it produces sentence embeddings of low quality. This means that the model might not be able to accurately capture the meaning and context of sentences, which can lead to poor performance in tasks like clustering or semantic search.
- Limited Dimensionality: The Current Model maps sentences and paragraphs to a
768-dimensional dense vector space. While this might seem like a lot, it’s actually a relatively limited dimensionality compared to other models. This can make it harder for the model to capture complex relationships between sentences. - Limited Input Length: The model has a maximum input length of
128tokens. This means that if you try to input a sentence or paragraph that’s longer than that, it will get truncated. This can be a problem if you’re working with longer texts.
Example Use Cases
Despite its limitations, the Current Model can still be used for tasks like:
- Clustering similar sentences together
- Performing semantic search on a large corpus of text
- Generating sentence embeddings for downstream tasks
However, keep in mind that the model’s performance might not be as good as other models, and you might need to adjust your expectations accordingly.
Code Examples
You can use the Current Model with the sentence-transformers library:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/bert-base-nli-mean-tokens')
embeddings = model.encode(sentences)
print(embeddings)
Alternatively, you can use the transformers library:
from transformers import AutoTokenizer, AutoModel
import torch
# Define a function for mean pooling
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 we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/bert-base-nli-mean-tokens')
model = AutoModel.from_pretrained('sentence-transformers/bert-base-nli-mean-tokens')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
Note: This model is deprecated and produces sentence embeddings of low quality. It’s recommended to use other sentence embedding models instead.
