Text Classification
Transformers
PyTorch
xlm-roberta
classification
Generated from Trainer
text-embeddings-inference
Instructions to use Atram11/ClassificationLanguage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Atram11/ClassificationLanguage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Atram11/ClassificationLanguage")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Atram11/ClassificationLanguage") model = AutoModelForSequenceClassification.from_pretrained("Atram11/ClassificationLanguage", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,642 Bytes
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"_name_or_path": "qanastek/51-languages-classifier",
"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": "af-ZA",
"1": "am-ET",
"2": "ar-SA",
"3": "az-AZ",
"4": "bn-BD",
"5": "cy-GB",
"6": "da-DK",
"7": "de-DE",
"8": "el-GR",
"9": "en-US",
"10": "es-ES",
"11": "fa-IR",
"12": "fi-FI",
"13": "fr-FR",
"14": "he-IL",
"15": "hi-IN",
"16": "hu-HU",
"17": "hy-AM",
"18": "id-ID",
"19": "is-IS",
"20": "it-IT",
"21": "ja-JP",
"22": "jv-ID",
"23": "ka-GE",
"24": "km-KH",
"25": "kn-IN",
"26": "ko-KR",
"27": "lv-LV",
"28": "ml-IN",
"29": "mn-MN",
"30": "ms-MY",
"31": "my-MM",
"32": "nb-NO",
"33": "nl-NL",
"34": "pl-PL",
"35": "pt-PT",
"36": "ro-RO",
"37": "ru-RU",
"38": "sl-SL",
"39": "sq-AL",
"40": "sv-SE",
"41": "sw-KE",
"42": "ta-IN",
"43": "te-IN",
"44": "th-TH",
"45": "tl-PH",
"46": "tr-TR",
"47": "ur-PK",
"48": "vi-VN",
"49": "zh-CN",
"50": "zh-TW"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"af-ZA": 0,
"am-ET": 1,
"ar-SA": 2,
"az-AZ": 3,
"bn-BD": 4,
"cy-GB": 5,
"da-DK": 6,
"de-DE": 7,
"el-GR": 8,
"en-US": 9,
"es-ES": 10,
"fa-IR": 11,
"fi-FI": 12,
"fr-FR": 13,
"he-IL": 14,
"hi-IN": 15,
"hu-HU": 16,
"hy-AM": 17,
"id-ID": 18,
"is-IS": 19,
"it-IT": 20,
"ja-JP": 21,
"jv-ID": 22,
"ka-GE": 23,
"km-KH": 24,
"kn-IN": 25,
"ko-KR": 26,
"lv-LV": 27,
"ml-IN": 28,
"mn-MN": 29,
"ms-MY": 30,
"my-MM": 31,
"nb-NO": 32,
"nl-NL": 33,
"pl-PL": 34,
"pt-PT": 35,
"ro-RO": 36,
"ru-RU": 37,
"sl-SL": 38,
"sq-AL": 39,
"sv-SE": 40,
"sw-KE": 41,
"ta-IN": 42,
"te-IN": 43,
"th-TH": 44,
"tl-PH": 45,
"tr-TR": 46,
"ur-PK": 47,
"vi-VN": 48,
"zh-CN": 49,
"zh-TW": 50
},
"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": "4.33.3",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 250002
}
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