Instructions to use nlpaueb/sec-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpaueb/sec-bert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nlpaueb/sec-bert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-base") model = AutoModelForPreTraining.from_pretrained("nlpaueb/sec-bert-base") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -53,6 +53,7 @@ model = AutoModel.from_pretrained("nlpaueb/sec-bert-base")
|
|
| 53 |
| Sample | Masked Token |
|
| 54 |
| --------------------------------------------------- | ------------ |
|
| 55 |
| Total net sales [MASK] 2% or $5.4 billion during 2019 compared to 2018. | decreased
|
|
|
|
| 56 |
| Model | Predictions (Probability) |
|
| 57 |
| --------------------------------------------------- | ------------ |
|
| 58 |
| **BERT-BASE-UNCASED** | increased (0.221), were (0.131), are (0.103), rose (0.075), of (0.058)
|
|
|
|
| 53 |
| Sample | Masked Token |
|
| 54 |
| --------------------------------------------------- | ------------ |
|
| 55 |
| Total net sales [MASK] 2% or $5.4 billion during 2019 compared to 2018. | decreased
|
| 56 |
+
|
| 57 |
| Model | Predictions (Probability) |
|
| 58 |
| --------------------------------------------------- | ------------ |
|
| 59 |
| **BERT-BASE-UNCASED** | increased (0.221), were (0.131), are (0.103), rose (0.075), of (0.058)
|