Text Classification
Transformers
Safetensors
Kabyle
ber
xlm-roberta
kabyle
tamazight
emotion-classification
sentiment-analysis
low-resource
cross-lingual-transfer
text-embeddings-inference
Instructions to use boffire/kabyle-emotion-xlmr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boffire/kabyle-emotion-xlmr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="boffire/kabyle-emotion-xlmr")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("boffire/kabyle-emotion-xlmr") model = AutoModelForSequenceClassification.from_pretrained("boffire/kabyle-emotion-xlmr") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "XLMRobertaForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "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-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.0.0", | |
| "type_vocab_size": 1, | |
| "use_cache": false, | |
| "vocab_size": 250002 | |
| } | |