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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 789 Bytes
7378580 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"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
}
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