Instructions to use nielsr/tapas-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nielsr/tapas-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nielsr/tapas-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nielsr/tapas-base") model = AutoModel.from_pretrained("nielsr/tapas-base") - Notebooks
- Google Colab
- Kaggle
Fix typo
Browse files
README.md
CHANGED
|
@@ -55,7 +55,7 @@ then of the form:
|
|
| 55 |
[CLS] Sentence [SEP] Flattened table [SEP]
|
| 56 |
```
|
| 57 |
|
| 58 |
-
###
|
| 59 |
|
| 60 |
The model was pre-trained on 32 Cloud TPU v3 cores for 1,000,000 steps with maximum sequence length 512 and batch size of 512.
|
| 61 |
In this setup, pre-training on MLM only takes around 3 days. Aditionally, the model has been further pre-trained on a second task (table entailment). See the original TAPAS [paper](https://www.aclweb.org/anthology/2020.acl-main.398/) and the [follow-up paper](https://www.aclweb.org/anthology/2020.findings-emnlp.27/) for more details.
|
|
|
|
| 55 |
[CLS] Sentence [SEP] Flattened table [SEP]
|
| 56 |
```
|
| 57 |
|
| 58 |
+
### Pre-training
|
| 59 |
|
| 60 |
The model was pre-trained on 32 Cloud TPU v3 cores for 1,000,000 steps with maximum sequence length 512 and batch size of 512.
|
| 61 |
In this setup, pre-training on MLM only takes around 3 days. Aditionally, the model has been further pre-trained on a second task (table entailment). See the original TAPAS [paper](https://www.aclweb.org/anthology/2020.acl-main.398/) and the [follow-up paper](https://www.aclweb.org/anthology/2020.findings-emnlp.27/) for more details.
|