Instructions to use google/tapas-large-finetuned-wtq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-large-finetuned-wtq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="google/tapas-large-finetuned-wtq")# Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("google/tapas-large-finetuned-wtq") model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-large-finetuned-wtq", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Fix typo
Browse files- config.json +1 -1
config.json
CHANGED
|
@@ -31,7 +31,7 @@
|
|
| 31 |
"max_num_rows": 64,
|
| 32 |
"max_position_embeddings": 1024,
|
| 33 |
"model_type": "tapas",
|
| 34 |
-
no_aggregation_label_index": 0,
|
| 35 |
"num_aggregation_labels": 4,
|
| 36 |
"num_attention_heads": 16,
|
| 37 |
"num_hidden_layers": 24,
|
|
|
|
| 31 |
"max_num_rows": 64,
|
| 32 |
"max_position_embeddings": 1024,
|
| 33 |
"model_type": "tapas",
|
| 34 |
+
"no_aggregation_label_index": 0,
|
| 35 |
"num_aggregation_labels": 4,
|
| 36 |
"num_attention_heads": 16,
|
| 37 |
"num_hidden_layers": 24,
|