Sentence Similarity
Transformers
PyTorch
Chinese
bert
feature-extraction
PEG
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use TownsWu/PEG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TownsWu/PEG with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("TownsWu/PEG") model = AutoModel.from_pretrained("TownsWu/PEG", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -10,11 +10,6 @@ tags:
|
|
| 10 |
license: apache-2.0
|
| 11 |
library_name: transformers
|
| 12 |
---
|
| 13 |
-
# Model Card for Model ID
|
| 14 |
-
|
| 15 |
-
<!-- Provide a quick summary of what the model is/does. -->
|
| 16 |
-
|
| 17 |
-
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
|
| 18 |
|
| 19 |
## Model Details
|
| 20 |
We propose the PEG model (a Progressively Learned Textual Embedding), which progressively adjusts the weights of samples contributing to the loss within an extremely large batch, based on the difficulty levels of negative samples.
|
|
|
|
| 10 |
license: apache-2.0
|
| 11 |
library_name: transformers
|
| 12 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 13 |
|
| 14 |
## Model Details
|
| 15 |
We propose the PEG model (a Progressively Learned Textual Embedding), which progressively adjusts the weights of samples contributing to the loss within an extremely large batch, based on the difficulty levels of negative samples.
|