Text Classification
Transformers
Safetensors
Chinese
bert
agent
nlp
chinese
sentiment-analysis
emotion
regression
vad
valence-arousal-dominance
macbert
text-embeddings-inference
Instructions to use Pectics/vad-macbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pectics/vad-macbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Pectics/vad-macbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Pectics/vad-macbert") model = AutoModelForSequenceClassification.from_pretrained("Pectics/vad-macbert") - Notebooks
- Google Colab
- Kaggle
Modified metadata
Browse files
README.md
CHANGED
|
@@ -9,6 +9,16 @@ base_model:
|
|
| 9 |
pipeline_tag: text-classification
|
| 10 |
tags:
|
| 11 |
- agent
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 12 |
---
|
| 13 |
|
| 14 |
# vad-macbert
|
|
|
|
| 9 |
pipeline_tag: text-classification
|
| 10 |
tags:
|
| 11 |
- agent
|
| 12 |
+
- nlp
|
| 13 |
+
- chinese
|
| 14 |
+
- sentiment-analysis
|
| 15 |
+
- emotion
|
| 16 |
+
- regression
|
| 17 |
+
- vad
|
| 18 |
+
- valence-arousal-dominance
|
| 19 |
+
- transformers
|
| 20 |
+
- bert
|
| 21 |
+
- macbert
|
| 22 |
---
|
| 23 |
|
| 24 |
# vad-macbert
|