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 tokenizer_config.json from MarcelM/CSEBERT-Emotion-Detection: direct link, hf CLI and curl.
- Browser
- Download file 428 Bytes
-
https://huggingface.co/MarcelM/CSEBERT-Emotion-Detection/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://MarcelM/CSEBERT-Emotion-Detection/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/MarcelM/CSEBERT-Emotion-Detection/resolve/main/tokenizer_config.json
428 Bytes
| { | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": false, | |
| "mask_token": "[MASK]", | |
| "max_len": 512, | |
| "model_max_length": 512, | |
| "name_or_path": "EMBEDDIA/crosloengual-bert", | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": null, | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |