Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Nathamon/bert-finetuned-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nathamon/bert-finetuned-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nathamon/bert-finetuned-mrpc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Nathamon/bert-finetuned-mrpc") model = AutoModelForSequenceClassification.from_pretrained("Nathamon/bert-finetuned-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer
Browse files- tokenizer.json +0 -0
- tokenizer_config.json +1 -1
- vocab.txt +0 -0
tokenizer.json
CHANGED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
CHANGED
|
@@ -43,7 +43,7 @@
|
|
| 43 |
},
|
| 44 |
"clean_up_tokenization_spaces": true,
|
| 45 |
"cls_token": "[CLS]",
|
| 46 |
-
"do_lower_case":
|
| 47 |
"mask_token": "[MASK]",
|
| 48 |
"model_max_length": 512,
|
| 49 |
"pad_token": "[PAD]",
|
|
|
|
| 43 |
},
|
| 44 |
"clean_up_tokenization_spaces": true,
|
| 45 |
"cls_token": "[CLS]",
|
| 46 |
+
"do_lower_case": false,
|
| 47 |
"mask_token": "[MASK]",
|
| 48 |
"model_max_length": 512,
|
| 49 |
"pad_token": "[PAD]",
|
vocab.txt
CHANGED
|
The diff for this file is too large to render.
See raw diff
|
|
|