Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use ratish/DBERT_CleanDesc_Collision_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ratish/DBERT_CleanDesc_Collision_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ratish/DBERT_CleanDesc_Collision_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ratish/DBERT_CleanDesc_Collision_v2") model = AutoModelForSequenceClassification.from_pretrained("ratish/DBERT_CleanDesc_Collision_v2") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#1
by librarian-bot - opened
README.md
CHANGED
|
@@ -2,6 +2,7 @@
|
|
| 2 |
license: apache-2.0
|
| 3 |
tags:
|
| 4 |
- generated_from_keras_callback
|
|
|
|
| 5 |
model-index:
|
| 6 |
- name: ratish/DBERT_CleanDesc_Collision_v2
|
| 7 |
results: []
|
|
|
|
| 2 |
license: apache-2.0
|
| 3 |
tags:
|
| 4 |
- generated_from_keras_callback
|
| 5 |
+
base_model: distilbert-base-uncased
|
| 6 |
model-index:
|
| 7 |
- name: ratish/DBERT_CleanDesc_Collision_v2
|
| 8 |
results: []
|