Instructions to use binwang/bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binwang/bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="binwang/bert-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("binwang/bert-base-uncased") model = AutoModelForMaskedLM.from_pretrained("binwang/bert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Document historical SBERT-WK checkpoint and maintenance status
Browse files
README.md
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- historical-research
|
| 4 |
+
- sbert-wk
|
| 5 |
+
---
|
| 6 |
+
|
| 7 |
+
# Historical research checkpoint — SBERT-WK (2020)
|
| 8 |
+
|
| 9 |
+
**No longer actively maintained. Retained for reproducibility of the original work.**
|
| 10 |
+
|
| 11 |
+
This repository hosts the original 12-layer BERT checkpoint used in the SBERT-WK experiments. It is a supporting checkpoint for the sentence embedding method.
|
| 12 |
+
|
| 13 |
+
For the paper, original software environment, and reproduction instructions, see [SBERT-WK: A Sentence Embedding Method by Dissecting BERT-based Word Models](https://github.com/BinWang28/SBERT-WK-Sentence-Embedding).
|
| 14 |
+
|