Instructions to use jplu/tf-flaubert-small-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jplu/tf-flaubert-small-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jplu/tf-flaubert-small-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jplu/tf-flaubert-small-cased") model = AutoModelForMaskedLM.from_pretrained("jplu/tf-flaubert-small-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,493 Bytes
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"amp": 1,
"architectures": [
"FlaubertWithLMHeadModel"
],
"asm": false,
"attention_dropout": 0.1,
"bos_index": 0,
"bos_token_id": 0,
"bptt": 512,
"causal": false,
"clip_grad_norm": 5,
"dropout": 0.1,
"emb_dim": 512,
"embed_init_std": 0.02209708691207961,
"encoder_only": true,
"end_n_top": 5,
"eos_index": 1,
"fp16": true,
"gelu_activation": true,
"group_by_size": true,
"id2lang": {
"0": "fr"
},
"init_std": 0.02,
"is_encoder": true,
"lang2id": {
"fr": 0
},
"lang_id": 0,
"langs": [
"fr"
],
"layer_norm_eps": 1e-06,
"layerdrop": 0.2,
"lg_sampling_factor": -1,
"lgs": "fr",
"mask_index": 5,
"mask_token_id": 0,
"max_batch_size": 0,
"max_position_embeddings": 512,
"max_vocab": -1,
"mlm_steps": [
[
"fr",
null
]
],
"model_type": "flaubert",
"n_heads": 8,
"n_langs": 1,
"n_layers": 6,
"pad_index": 2,
"pad_token_id": 2,
"pre_norm": true,
"sample_alpha": 0,
"share_inout_emb": true,
"sinusoidal_embeddings": false,
"start_n_top": 5,
"summary_activation": null,
"summary_first_dropout": 0.1,
"summary_proj_to_labels": true,
"summary_type": "first",
"summary_use_proj": true,
"tokens_per_batch": -1,
"unk_index": 3,
"use_lang_emb": true,
"vocab_size": 68729,
"word_blank": 0,
"word_dropout": 0,
"word_keep": 0.1,
"word_mask": 0.8,
"word_mask_keep_rand": "0.8,0.1,0.1",
"word_pred": 0.15,
"word_rand": 0.1,
"word_shuffle": 0
}
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