Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Chinese
ernie
feature-extraction
text2vec
Instructions to use shibing624/text2vec-base-chinese-paraphrase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use shibing624/text2vec-base-chinese-paraphrase with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shibing624/text2vec-base-chinese-paraphrase") sentences = [ "那是 個快樂的人", "那是 條快樂的狗", "那是 個非常幸福的人", "今天是晴天" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use shibing624/text2vec-base-chinese-paraphrase with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("shibing624/text2vec-base-chinese-paraphrase") model = AutoModel.from_pretrained("shibing624/text2vec-base-chinese-paraphrase", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download training_progress_scores.csv from shibing624/text2vec-base-chinese-paraphrase: direct link, hf CLI and curl.
- Browser
- Download file 602 Bytes
-
https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase/resolve/main/training_progress_scores.csv
- Command line
-
hf download hf://shibing624/text2vec-base-chinese-paraphrase/training_progress_scores.csv
-
curl -L -o training_progress_scores.csv https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase/resolve/main/training_progress_scores.csv
602 Bytes
| global_step,train_loss,eval_spearman,eval_pearson | |
| 726,4.268328666687012,0.4961976634488731,0.5098699767582849 | |
| 1452,4.030226707458496,0.5165544906672884,0.5108012677326882 | |
| 2178,3.929202079772949,0.5216436974718923,0.5312741671915948 | |
| 2904,3.562161684036255,0.5470897314949114,0.5322684862992731 | |
| 3630,3.8007586002349854,0.5521789382995153,0.5280511704101458 | |
| 4356,3.7629058361053467,0.5750803689202325,0.5509343153593886 | |
| 5082,3.2100651264190674,0.5547235417018171,0.5438526474709305 | |
| 5808,3.0945613384246826,0.557268145104119,0.5373790240653944 | |
| 6534,3.2587833404541016,0.5623573519087228,0.5427030620948493 | |