Text Classification
Transformers
TensorFlow
English
distilbert
text classification
transformer
sentiment analysis
TensorFlow
text-embeddings-inference
Instructions to use dancingninjas/sentiment-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dancingninjas/sentiment-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dancingninjas/sentiment-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dancingninjas/sentiment-model") model = AutoModelForSequenceClassification.from_pretrained("dancingninjas/sentiment-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "dancingninjas/sentiment-model", | |
| "activation": "gelu", | |
| "architectures": [ | |
| "TFAutoModelForSequenceClassification" | |
| ], | |
| "attention_dropout": 0.1, | |
| "dim": 768, | |
| "dropout": 0.1, | |
| "hidden_dim": 3072, | |
| "initializer_range": 0.02, | |
| "max_position_embeddings": 512, | |
| "model_type": "distilbert", | |
| "n_heads": 12, | |
| "n_layers": 6, | |
| "pad_token_id": 0, | |
| "problem_type": "single_label_classification", | |
| "seq_classif_dropout": 0.2, | |
| "transformers_version": "4.26.1", | |
| "vocab_size": 30522, | |
| "description": "A fine-tuned DistilBERT model for sentiment analysis.", | |
| "tags": ["text-classification", "sentiment-analysis"], | |
| "pipeline_tag": "text-classification", | |
| "framework": "transformers", | |
| "pytorch_version": "1.8.1", | |
| "tensorflow_version": "2.7.0", | |
| "license": "MIT" | |
| } | |