Instructions to use hf-tiny-model-private/tiny-random-FNetForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-FNetForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-tiny-model-private/tiny-random-FNetForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-FNetForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-FNetForMaskedLM") - Notebooks
- Google Colab
- Kaggle
File size: 544 Bytes
dbca388 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"architectures": [
"FNetForMaskedLM"
],
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 32,
"initializer_range": 0.02,
"intermediate_size": 37,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "fnet",
"num_hidden_layers": 5,
"pad_token_id": 3,
"torch_dtype": "float32",
"tpu_short_seq_length": 7,
"transformers_version": "4.28.0.dev0",
"type_vocab_size": 16,
"use_tpu_fourier_optimizations": false,
"vocab_size": 32000
}
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