Text Classification
Transformers
Safetensors
Slovenian
English
Croatian
bert
emotion-recognition
multi-label-classification
slovene
ekman
text-embeddings-inference
Instructions to use MarcelM/CSEBERT-Emotion-Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MarcelM/CSEBERT-Emotion-Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MarcelM/CSEBERT-Emotion-Detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MarcelM/CSEBERT-Emotion-Detection") model = AutoModelForSequenceClassification.from_pretrained("MarcelM/CSEBERT-Emotion-Detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from MarcelM/CSEBERT-Emotion-Detection: direct link, hf CLI and curl.
- Browser
- Download file 1.12 kB
-
https://huggingface.co/MarcelM/CSEBERT-Emotion-Detection/resolve/main/config.json
- Command line
-
hf download hf://MarcelM/CSEBERT-Emotion-Detection/config.json
-
curl -L -o config.json https://huggingface.co/MarcelM/CSEBERT-Emotion-Detection/resolve/main/config.json
1.12 kB
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "anger", | |
| "1": "disgust", | |
| "2": "fear", | |
| "3": "joy", | |
| "4": "sadness", | |
| "5": "surprise", | |
| "6": "neutral" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": false, | |
| "label2id": { | |
| "anger": 0, | |
| "disgust": 1, | |
| "fear": 2, | |
| "joy": 3, | |
| "neutral": 6, | |
| "sadness": 4, | |
| "surprise": 5 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "multi_label_classification", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.17.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 49601 | |
| } | |