Text Classification
Transformers
PyTorch
Danish
bert
danish
sentiment
Maltehb/danish-bert-botxo
Helsinki-NLP/opus-mt-en-da
go-emotion
Certainly
text-embeddings-inference
Instructions to use RJuro/Da-HyggeBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RJuro/Da-HyggeBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RJuro/Da-HyggeBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RJuro/Da-HyggeBERT") model = AutoModelForSequenceClassification.from_pretrained("RJuro/Da-HyggeBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from RJuro/Da-HyggeBERT: direct link, hf CLI and curl.
- Browser
- Download file 2.18 kB
-
https://huggingface.co/RJuro/Da-HyggeBERT/resolve/main/config.json
- Command line
-
hf download hf://RJuro/Da-HyggeBERT/config.json
-
curl -L -o config.json https://huggingface.co/RJuro/Da-HyggeBERT/resolve/main/config.json
2.18 kB
| { | |
| "_name_or_path": "/content/drive/MyDrive/Colab/dk-go-emotions/model", | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "directionality": "bidi", | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "beundring", | |
| "1": "forn\u00f8jelse", | |
| "2": "vrede", | |
| "3": "irritation", | |
| "4": "medhold", | |
| "5": "omsorg", | |
| "6": "forvirring", | |
| "7": "nysgerrighed", | |
| "8": "beg\u00e6r", | |
| "9": "skuffelse", | |
| "10": "misbilligelse", | |
| "11": "afsky", | |
| "12": "forlegenhed", | |
| "13": "sp\u00e6nding", | |
| "14": "frygt", | |
| "15": "taknemmelighed", | |
| "16": "sorg", | |
| "17": "gl\u00e6de", | |
| "18": "k\u00e6rlighed", | |
| "19": "nerv\u00f8sitet", | |
| "20": "optimisme", | |
| "21": "stolthed", | |
| "22": "indsigt", | |
| "23": "lettelse", | |
| "24": "fortrydelse", | |
| "25": "tristhed", | |
| "26": "overraskelse", | |
| "27": "neutral" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "afsky": 11, | |
| "beg\u00e6r": 8, | |
| "beundring": 0, | |
| "forlegenhed": 12, | |
| "forn\u00f8jelse": 1, | |
| "fortrydelse": 24, | |
| "forvirring": 6, | |
| "frygt": 14, | |
| "gl\u00e6de": 17, | |
| "indsigt": 22, | |
| "irritation": 3, | |
| "k\u00e6rlighed": 18, | |
| "lettelse": 23, | |
| "medhold": 4, | |
| "misbilligelse": 10, | |
| "nerv\u00f8sitet": 19, | |
| "neutral": 27, | |
| "nysgerrighed": 7, | |
| "omsorg": 5, | |
| "optimisme": 20, | |
| "overraskelse": 26, | |
| "skuffelse": 9, | |
| "sorg": 16, | |
| "sp\u00e6nding": 13, | |
| "stolthed": 21, | |
| "taknemmelighed": 15, | |
| "tristhed": 25, | |
| "vrede": 2 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "pooler_fc_size": 768, | |
| "pooler_num_attention_heads": 12, | |
| "pooler_num_fc_layers": 3, | |
| "pooler_size_per_head": 128, | |
| "pooler_type": "first_token_transform", | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.20.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 32000 | |
| } | |