Feature Extraction
Transformers
TensorFlow
English
roberta
code
tensorflow
LSTM
Sentiment analysis
text-embeddings-inference
Instructions to use perfectmind/sentiment_analysis_twitter_537K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use perfectmind/sentiment_analysis_twitter_537K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="perfectmind/sentiment_analysis_twitter_537K")# Load model directly from transformers import AutoTokenizer, TF_AutoModel tokenizer = AutoTokenizer.from_pretrained("perfectmind/sentiment_analysis_twitter_537K") model = TF_AutoModel.from_pretrained("perfectmind/sentiment_analysis_twitter_537K", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "Deep_Learning_Twitter_Sentiment_Analysis_Model", | |
| "activation": "sigmoid", | |
| "architectures": [ | |
| "TFBertModel" | |
| ], | |
| "dim": 128, | |
| "label2id": { | |
| "negative": 0, | |
| "neutral": 0.5, | |
| "positive": 1 | |
| }, | |
| "dropout": 0.5, | |
| "hidden_dim": 64, | |
| "model_type": "roberta", | |
| "initializer_range": 0.02, | |
| "max_position_embeddings": 537000, | |
| "n_heads": 8, | |
| "n_layers": 2, | |
| "pad_token_id": 0, | |
| "qa_dropout": 0.1, | |
| "max_length ": 40, | |
| "tie_weights_": true, | |
| "vocab_size": 512711 | |
| } |