đ DunnBC22/sentence-t5-base-FT-Quora_Sentence_Similarity-LG
This is a sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space. It can be used for tasks such as clustering or semantic search.
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Semantic_Similarity/Semantic%20Similarity-base.ipynb
đ Quick Start
đĻ Installation
Using this model becomes easy when you have sentence-transformers installed. You can install it with the following command:
pip install -U sentence-transformers
đģ Usage Examples
Basic Usage
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('DunnBC22/sentence-t5-base-FT-Quora_Sentence_Similarity-LG')
embeddings = model.encode(sentences)
print(embeddings)
đ Documentation
Evaluation Results
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
Property |
Details |
Model Type |
Sentence-transformers |
Training Data |
Not specified |
Metrics |
Accuracy, F1, Recall, Precision |
Metric |
Measure |
Value |
Notes |
Accuracy |
Cosine-Similarity |
85.93 |
Threshold: 0.8320 |
F1 |
Cosine-Similarity |
82.89 |
Threshold: 0.8178 |
Precision |
Cosine-Similarity |
77.43 |
- |
Recall |
Cosine-Similarity |
89.18 |
- |
Average Precision |
Cosine-Similarity |
87.13 |
- |
Accuracy |
Manhattan-Distance |
85.95 |
Threshold: 12.7721 |
F1 |
Manhattan-Distance |
82.89 |
Threshold: 13.5008 |
Precision |
Manhattan-Distance |
76.91 |
- |
Recall |
Manhattan-Distance |
89.89 |
- |
Average Precision |
Manhattan-Distance |
87.13 |
- |
Accuracy |
Euclidean-Distance |
85.93 |
Threshold: 0.5797 |
F1 |
Euclidean-Distance |
82.89 |
Threshold: 0.6037 |
Precision |
Euclidean-Distance |
77.43 |
- |
Recall |
Euclidean-Distance |
89.18 |
- |
Average Precision |
Euclidean-Distance |
87.13 |
- |
Accuracy |
Dot-Product |
85.93 |
Threshold: 0.8320 |
F1 |
Dot-Product |
82.89 |
Threshold: 0.8178 |
Precision |
Dot-Product |
77.43 |
- |
Recall |
Dot-Product |
89.18 |
- |
Average Precision |
Dot-Product |
87.14 |
- |
đ§ Technical Details
Training
The model was trained with the parameters:
DataLoader:
torch.utils.data.dataloader.DataLoader
of length 4673 with parameters:
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
Loss:
sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss
Parameters of the fit()-Method:
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2,
"weight_decay": 0.01
}
Potential Improvements
One way to improve the results of this model is to use a larger checkpoint of T5. This was trained with the T5-base checkpoint.
The larger checkpoints are:
Checkpoint |
# of Train Params |
T5-Base |
220 Million* |
T5-Large |
770 Million |
T5-3B |
3 Billion |
T5-11B |
11 Billion |
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 34, 'do_lower_case': False}) with Transformer model: T5EncoderModel
(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})
(2): Dense({'in_features': 768, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
(3): Normalize()
)
đ License
No license information provided.
Citing & Authors
Dataset Source: https://www.kaggle.com/datasets/quora/question-pairs-dataset