Instructions to use deepset/gelectra-base-germanquad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/gelectra-base-germanquad with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="deepset/gelectra-base-germanquad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/gelectra-base-germanquad") model = AutoModelForQuestionAnswering.from_pretrained("deepset/gelectra-base-germanquad", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -52,7 +52,7 @@ docs = [
|
|
| 52 |
Document(content="python ist eine beliebte Programmiersprache"),
|
| 53 |
]
|
| 54 |
|
| 55 |
-
reader = ExtractiveReader(model="deepset/
|
| 56 |
reader.warm_up()
|
| 57 |
|
| 58 |
question = "What is a popular programming language?"
|
|
@@ -65,7 +65,7 @@ For a complete example with an extractive question answering pipeline that scale
|
|
| 65 |
```python
|
| 66 |
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
|
| 67 |
|
| 68 |
-
model_name = "deepset/
|
| 69 |
|
| 70 |
# a) Get predictions
|
| 71 |
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
|
|
|
|
| 52 |
Document(content="python ist eine beliebte Programmiersprache"),
|
| 53 |
]
|
| 54 |
|
| 55 |
+
reader = ExtractiveReader(model="deepset/gelectra-base-germanquad")
|
| 56 |
reader.warm_up()
|
| 57 |
|
| 58 |
question = "What is a popular programming language?"
|
|
|
|
| 65 |
```python
|
| 66 |
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
|
| 67 |
|
| 68 |
+
model_name = "deepset/gelectra-base-germanquad"
|
| 69 |
|
| 70 |
# a) Get predictions
|
| 71 |
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
|