Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use bernardocecchetto/QQ_NLP_MODEL_collection_newProcessingData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bernardocecchetto/QQ_NLP_MODEL_collection_newProcessingData with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bernardocecchetto/QQ_NLP_MODEL_collection_newProcessingData")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bernardocecchetto/QQ_NLP_MODEL_collection_newProcessingData") model = AutoModelForSequenceClassification.from_pretrained("bernardocecchetto/QQ_NLP_MODEL_collection_newProcessingData", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload TFDistilBertForSequenceClassification
Browse files- tf_model.h5 +1 -1
tf_model.h5
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 626547656
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:eb89696328b9697c9ff2468c1f3f56677f9874ea20d9e441c3c5c534e65d7840
|
| 3 |
size 626547656
|