Ag Nli Bert Mpnet Base Uncased Sentence Similarity V1

🚀 BERT/MPnet base model (uncased)

This model is a sentence-transformers model that maps sentences and paragraphs into a 768-dimensional dense vector space. It can be used for tasks such as clustering or semantic search.

🚀 Quick Start

✨ Features

• Maps sentences and paragraphs to a 768-dimensional dense vector space.
• Suitable for clustering and semantic search tasks.

📦 Installation

To use this model, you need to install sentence-transformers:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

If you have sentence-transformers installed, you can use the model as follows:

from sentence_transformers import SentenceTransformer, util


model = SentenceTransformer('abbasgolestani/ag-nli-bert-mpnet-base-uncased-sentence-similarity-v1') nli-mpnet-base-v2

# Two lists of sentences
sentences1 = ['I am honored to be given the opportunity to help make our company better',
             'I love my job and what I do here',
             'I am excited about our company’s vision']

sentences2 = ['I am hopeful about the future of our company',
              'My work is aligning with my passion',
              'Definitely our company vision will be the next breakthrough to change the world and I’m so happy and proud to work here']

#Compute embedding for both lists
embeddings1 = model.encode(sentences1, convert_to_tensor=True)
embeddings2 = model.encode(sentences2, convert_to_tensor=True)

#Compute cosine-similarities
cosine_scores = util.cos_sim(embeddings1, embeddings2)

#Output the pairs with their score
for i in range(len(sentences1)):
    print("{} \t\t {} \t\t Score: {:.4f}".format(sentences1[i], sentences2[i], cosine_scores[i][i]))

Advanced Usage

Without sentence-transformers, you can use the model like this: First, pass your input through the transformer model, then apply the right pooling-operation on top of the contextualized word embeddings.

from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    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('abbasgolestani/ag-nli-bert-mpnet-base-uncased-sentence-similarity-v1')
model = AutoModel.from_pretrained('abbasgolestani/ag-nli-bert-mpnet-base-uncased-sentence-similarity-v1')

# 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, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

📚 Documentation

Evaluation Results

This model has been evaluated on a local dataset with 1000 sentence pairs. The algorithm achieved an accuracy of 82% on this dataset.

Training

The model was trained with the following parameters:

DataLoader: torch.utils.data.dataloader.DataLoader of length 7 with parameters:

{'batch_size': 16, '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": 1,
    "evaluation_steps": 0,
    "evaluator": "NoneType",
    "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': 75, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (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})
)

Citing & Authors