Instructions to use Pclanglais/transcript-text-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pclanglais/transcript-text-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Pclanglais/transcript-text-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Pclanglais/transcript-text-analysis") model = AutoModelForSequenceClassification.from_pretrained("Pclanglais/transcript-text-analysis") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
**transcript-text-analysis** is an encoder model specialized for the classification of French news transcripts. The model is based on debertav3 and has been trained on 1,018 examples of annotated transcripts.
|
| 2 |
+
|
| 3 |
+
Given a text, transcript-text-analysis will generate the following classifications in French:
|
| 4 |
+
* Emotion
|
| 5 |
+
* Expression
|
| 6 |
+
* Intention
|
| 7 |
+
* Theme
|
| 8 |
+
* Tonalite
|