Feature Extraction
Transformers
PyTorch
English
bert
BERT
encoder
embeddings
TiME
size:s
text-embeddings-inference
Instructions to use dschulmeist/TiME-en-s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dschulmeist/TiME-en-s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dschulmeist/TiME-en-s")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dschulmeist/TiME-en-s") model = AutoModel.from_pretrained("dschulmeist/TiME-en-s", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -13,7 +13,7 @@ tags:
|
|
| 13 |
license: apache-2.0
|
| 14 |
teacher_model: FacebookAI/xlm-roberta-large
|
| 15 |
datasets:
|
| 16 |
-
- CulturaX
|
| 17 |
---
|
| 18 |
|
| 19 |
# TiME English (en, s)
|
|
|
|
| 13 |
license: apache-2.0
|
| 14 |
teacher_model: FacebookAI/xlm-roberta-large
|
| 15 |
datasets:
|
| 16 |
+
- uonlp/CulturaX
|
| 17 |
---
|
| 18 |
|
| 19 |
# TiME English (en, s)
|