Instructions to use ChillingDream/dap-mbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChillingDream/dap-mbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ChillingDream/dap-mbert-base")# Load model directly from transformers import AutoTokenizer, BertForRLM tokenizer = AutoTokenizer.from_pretrained("ChillingDream/dap-mbert-base") model = BertForRLM.from_pretrained("ChillingDream/dap-mbert-base") - Notebooks
- Google Colab
- Kaggle
File size: 1,330 Bytes
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"_name_or_path": "/mnt/bingadseuunifiedstorage/v-liziheng/result/xtreme-rlm-t2-target-enlm-fwd-ams0-bert-base-multilingual-cased.2",
"architectures": [
"BertForRLM"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"directionality": "bidi",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"langs": [
"af",
"ar",
"bg",
"bn",
"de",
"el",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"he",
"hi",
"hu",
"id",
"it",
"ja",
"jv",
"ka",
"kk",
"ko",
"ml",
"mr",
"nl",
"pt",
"ru",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ur",
"vi",
"zh"
],
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"n_langs": 38,
"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": "4.20.1",
"type_vocab_size": 2,
"use_cache": true,
"use_lang_emb": false,
"vocab_size": 119547
}
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