Text Classification
Transformers
PyTorch
TensorFlow
English
deberta-v2
Sentiment-Analysis
Hate-Speech_Detection
NLP
Multi-task
Instructions to use Vivek-Sham/deberta-multitask-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vivek-Sham/deberta-multitask-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Vivek-Sham/deberta-multitask-sentiment-analysis")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Vivek-Sham/deberta-multitask-sentiment-analysis", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "attention_heads": 8, | |
| "attention_probs_dropout_prob": 0.1, | |
| "dropout_rate": 0.3, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-07, | |
| "max_position_embeddings": 512, | |
| "max_relative_positions": -1, | |
| "model_type": "deberta-v2", | |
| "num_attention_heads": 12, | |
| "num_emotion_labels": 8, | |
| "num_hate_speech_labels": 2, | |
| "num_hidden_layers": 12, | |
| "num_lstm_units": 128, | |
| "num_polarity_labels": 4, | |
| "pad_token_id": 0, | |
| "pooler_dropout": 0, | |
| "pooler_hidden_act": "gelu", | |
| "pooler_hidden_size": 768, | |
| "pos_att_type": null, | |
| "position_biased_input": true, | |
| "relative_attention": false, | |
| "transformers_version": "4.44.2", | |
| "type_vocab_size": 0, | |
| "vocab_size": 128100 | |
| } | |