Text Classification
Transformers
PyTorch
TensorFlow
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use papluca/xlm-roberta-base-language-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use papluca/xlm-roberta-base-language-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="papluca/xlm-roberta-base-language-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("papluca/xlm-roberta-base-language-detection") model = AutoModelForSequenceClassification.from_pretrained("papluca/xlm-roberta-base-language-detection", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 1,494 Bytes
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"_name_or_path": "drive/MyDrive/Colab Notebooks/HuggingFace_course/HF_course_community_event/xlm-roberta-base-finetuned-language-detection",
"architectures": [
"XLMRobertaForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "ja",
"1": "nl",
"2": "ar",
"3": "pl",
"4": "de",
"5": "it",
"6": "pt",
"7": "tr",
"8": "es",
"9": "hi",
"10": "el",
"11": "ur",
"12": "bg",
"13": "en",
"14": "fr",
"15": "zh",
"16": "ru",
"17": "th",
"18": "sw",
"19": "vi"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"ar": 2,
"bg": 12,
"de": 4,
"el": 10,
"en": 13,
"es": 8,
"fr": 14,
"hi": 9,
"it": 5,
"ja": 0,
"nl": 1,
"pl": 3,
"pt": 6,
"ru": 16,
"sw": 18,
"th": 17,
"tr": 7,
"ur": 11,
"vi": 19,
"zh": 15
},
"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",
"torch_dtype": "float32",
"transformers_version": "4.12.5",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 250002
}
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