Text Classification
Transformers
Safetensors
Norwegian
Norwegian Bokmål
Norwegian Nynorsk
token-classification
custom_code
Instructions to use ltg/norbert3-base_sentence-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ltg/norbert3-base_sentence-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ltg/norbert3-base_sentence-sentiment", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("ltg/norbert3-base_sentence-sentiment", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from ltg/norbert3-base_sentence-sentiment: direct link, hf CLI and curl.
- Browser
- Download file 1.42 kB
-
https://huggingface.co/ltg/norbert3-base_sentence-sentiment/resolve/main/config.json
- Command line
-
hf download hf://ltg/norbert3-base_sentence-sentiment/config.json
-
curl -L -o config.json https://huggingface.co/ltg/norbert3-base_sentence-sentiment/resolve/main/config.json
1.42 kB
| { | |
| "_name_or_path": "/home/egil/gits_wsl/SANT_FinalStage/models/finetuned_norec_sentence-mixed__norbert3-base__06181104/checkpoint-540", | |
| "architectures": [ | |
| "NorbertForTokenClassification", | |
| "NorbertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "auto_map": { | |
| "AutoConfig": "configuration_norbert.NorbertConfig", | |
| "AutoModel": "modeling_norbert.NorbertModel", | |
| "AutoModelForMaskedLM": "modeling_norbert.NorbertForMaskedLM", | |
| "AutoModelForMultipleChoice": "modeling_norbert.NorbertForMultipleChoice", | |
| "AutoModelForQuestionAnswering": "modeling_norbert.NorbertForQuestionAnswering", | |
| "AutoModelForSequenceClassification": "modeling_norbert.NorbertForSequenceClassification", | |
| "AutoModelForTokenClassification": "modeling_norbert.NorbertForTokenClassification" | |
| }, | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "Negative", | |
| "1": "Positive", | |
| "2": "Neutral", | |
| "3": "Mixed" | |
| }, | |
| "intermediate_size": 2048, | |
| "label2id": { | |
| "Mixed": 3, | |
| "Negative": 0, | |
| "Neutral": 2, | |
| "Positive": 1 | |
| }, | |
| "layer_norm_eps": 1e-07, | |
| "max_position_embeddings": 512, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_all_encoded_layers": true, | |
| "position_bucket_size": 32, | |
| "problem_type": "single_label_classification", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.36.2", | |
| "vocab_size": 50000 | |
| } | |