Text Classification
Transformers
PyTorch
Safetensors
Enawené-Nawé
roberta
text-regression
anger
emotion
emotion intensity
text-embeddings-inference
Instructions to use garrettbaber/twitter-roberta-base-anger-intensity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use garrettbaber/twitter-roberta-base-anger-intensity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="garrettbaber/twitter-roberta-base-anger-intensity")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("garrettbaber/twitter-roberta-base-anger-intensity") model = AutoModelForSequenceClassification.from_pretrained("garrettbaber/twitter-roberta-base-anger-intensity", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from garrettbaber/twitter-roberta-base-anger-intensity: direct link, hf CLI and curl.
- Browser
- Download file 860 Bytes
-
https://huggingface.co/garrettbaber/twitter-roberta-base-anger-intensity/resolve/1c4db45f14c3eac8b04c2eda03bec9579d32982b/config.json
- Command line
-
hf download hf://garrettbaber/twitter-roberta-base-anger-intensity@1c4db45f14c3eac8b04c2eda03bec9579d32982b/config.json
-
curl -L -o config.json https://huggingface.co/garrettbaber/twitter-roberta-base-anger-intensity/resolve/1c4db45f14c3eac8b04c2eda03bec9579d32982b/config.json
860 Bytes
| { | |
| "_name_or_path": "AutoTrain", | |
| "_num_labels": 1, | |
| "architectures": [ | |
| "RobertaForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "target" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "target": "0" | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_length": 64, | |
| "max_position_embeddings": 514, | |
| "model_type": "roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 1, | |
| "padding": "max_length", | |
| "position_embedding_type": "absolute", | |
| "problem_type": "regression", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.29.2", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 50265 | |
| } | |