Text Classification
Transformers
ONNX
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use albertmartinez/bert-sdg-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use albertmartinez/bert-sdg-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="albertmartinez/bert-sdg-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("albertmartinez/bert-sdg-classification") model = AutoModelForSequenceClassification.from_pretrained("albertmartinez/bert-sdg-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
---
|
| 2 |
-
license:
|
| 3 |
base_model: google-bert/bert-base-uncased
|
| 4 |
tags:
|
| 5 |
- generated_from_trainer
|
|
@@ -60,4 +60,4 @@ The following hyperparameters were used during training:
|
|
| 60 |
- Transformers 4.42.4
|
| 61 |
- Pytorch 2.3.1+cu121
|
| 62 |
- Datasets 2.20.0
|
| 63 |
-
- Tokenizers 0.19.1
|
|
|
|
| 1 |
---
|
| 2 |
+
license: mit
|
| 3 |
base_model: google-bert/bert-base-uncased
|
| 4 |
tags:
|
| 5 |
- generated_from_trainer
|
|
|
|
| 60 |
- Transformers 4.42.4
|
| 61 |
- Pytorch 2.3.1+cu121
|
| 62 |
- Datasets 2.20.0
|
| 63 |
+
- Tokenizers 0.19.1
|