Text Classification
Transformers
Safetensors
English
deberta-v2
multi-label-classification
chat
message-enrichment
group-chat
deberta-v3
community-analytics
Eval Results (legacy)
text-embeddings-inference
Instructions to use eladlaor/chat-message-tagger-deberta-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eladlaor/chat-message-tagger-deberta-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eladlaor/chat-message-tagger-deberta-v3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eladlaor/chat-message-tagger-deberta-v3") model = AutoModelForSequenceClassification.from_pretrained("eladlaor/chat-message-tagger-deberta-v3") - Notebooks
- Google Colab
- Kaggle
| { | |
| "professional": 0.3500000000000001, | |
| "question": 0.8000000000000002, | |
| "experience_sharing": 0.6000000000000002, | |
| "resource": 0.6000000000000002, | |
| "opinion": 0.5500000000000002, | |
| "how_to": 0.6500000000000001, | |
| "humor": 0.7500000000000002, | |
| "announcement": 0.7500000000000002, | |
| "off_group_topic": 0.7500000000000002, | |
| "reaction": 0.7500000000000002, | |
| "substantive": 0.8000000000000002, | |
| "discussion_init": 0.5500000000000002, | |
| "emotional": 0.7500000000000002, | |
| "disagreement": 0.25000000000000006, | |
| "positive_reinforcement": 0.8000000000000002 | |
| } |