laszlokiss27
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Commit
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Parent(s):
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Upload 12 files
Browse files- README.md +15 -0
- config.json +30 -0
- decoder_model.onnx +3 -0
- encoder_model.onnx +3 -0
- encoder_model_quantified.onnx +3 -0
- init_decoder_model.onnx +3 -0
- init_decoder_quantified.onnx +3 -0
- ort_config.json +29 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +107 -0
- tokenizer.json +0 -0
- tokenizer_config.json +112 -0
README.md
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---
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language:
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- en
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tags:
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- grammar-correction
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license: mit
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datasets:
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- c4_200m
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---
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# T5-Efficient-TINY for grammar correction
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This is a [T5-Efficient-TINY](https://huggingface.co/google/t5-efficient-tiny) model that was trained on a subset of [C4_200M](https://ai.googleblog.com/2021/08/the-c4200m-synthetic-dataset-for.html) dataset to solve the grammar correction task in English. To bring additional errors, random typos were introduced to the input sentences using the [nlpaug](https://github.com/makcedward/nlpaug) library. Since the model was trained on only one task, there are no prefixes needed.
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The model was trained as a part of the project during the [Full Stack Deep Learning](https://fullstackdeeplearning.com/course/2022/) course. ONNX version of the model is deployed on the [site](https://edge-ai.vercel.app/models/grammar-check) and can be run directly in the browser.
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config.json
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{
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"_name_or_path": "google/t5-efficient-tiny",
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"d_ff": 1024,
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"d_kv": 64,
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"d_model": 256,
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"decoder_start_token_id": 0,
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"dense_act_fn": "relu",
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"feed_forward_proj": "relu",
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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"is_gated_act": false,
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"n_positions": 512,
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"num_decoder_layers": 4,
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"num_heads": 4,
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"num_layers": 4,
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
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"relative_attention_num_buckets": 32,
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"torch_dtype": "float32",
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"transformers_version": "4.26.0.dev0",
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"use_cache": true,
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"vocab_size": 32128
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}
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decoder_model.onnx
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version https://git-lfs.github.com/spec/v1
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size 80538560
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encoder_model.onnx
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encoder_model_quantified.onnx
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init_decoder_model.onnx
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init_decoder_quantified.onnx
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ort_config.json
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{
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"opset": null,
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"optimization": {},
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"optimum_version": "1.4.0",
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"quantization": {
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"activations_dtype": "QUInt8",
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"activations_symmetric": false,
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"format": "QOperator",
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"is_static": false,
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"mode": "IntegerOps",
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"nodes_to_exclude": [],
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"nodes_to_quantize": [],
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"operators_to_quantize": [
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"MatMul",
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"Add"
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],
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"per_channel": false,
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"qdq_add_pair_to_weight": false,
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"qdq_dedicated_pair": false,
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"qdq_op_type_per_channel_support_to_axis": {
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"MatMul": 1
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},
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"reduce_range": false,
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"weights_dtype": "QInt8",
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"weights_symmetric": true
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},
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"transformers_version": "4.21.2",
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"use_external_data_format": false
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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special_tokens_map.json
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}
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
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"<extra_id_67>",
|
71 |
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"<extra_id_68>",
|
72 |
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"<extra_id_69>",
|
73 |
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"<extra_id_70>",
|
74 |
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"<extra_id_71>",
|
75 |
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"<extra_id_72>",
|
76 |
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"<extra_id_73>",
|
77 |
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"<extra_id_74>",
|
78 |
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"<extra_id_75>",
|
79 |
+
"<extra_id_76>",
|
80 |
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"<extra_id_77>",
|
81 |
+
"<extra_id_78>",
|
82 |
+
"<extra_id_79>",
|
83 |
+
"<extra_id_80>",
|
84 |
+
"<extra_id_81>",
|
85 |
+
"<extra_id_82>",
|
86 |
+
"<extra_id_83>",
|
87 |
+
"<extra_id_84>",
|
88 |
+
"<extra_id_85>",
|
89 |
+
"<extra_id_86>",
|
90 |
+
"<extra_id_87>",
|
91 |
+
"<extra_id_88>",
|
92 |
+
"<extra_id_89>",
|
93 |
+
"<extra_id_90>",
|
94 |
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"<extra_id_91>",
|
95 |
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"<extra_id_92>",
|
96 |
+
"<extra_id_93>",
|
97 |
+
"<extra_id_94>",
|
98 |
+
"<extra_id_95>",
|
99 |
+
"<extra_id_96>",
|
100 |
+
"<extra_id_97>",
|
101 |
+
"<extra_id_98>",
|
102 |
+
"<extra_id_99>"
|
103 |
+
],
|
104 |
+
"eos_token": "</s>",
|
105 |
+
"extra_ids": 100,
|
106 |
+
"name_or_path": "visheratin/t5-efficient-tiny-grammar-correction",
|
107 |
+
"pad_token": "<pad>",
|
108 |
+
"sp_model_kwargs": {},
|
109 |
+
"special_tokens_map_file": "t5-efficient-tiny/special_tokens_map.json",
|
110 |
+
"tokenizer_class": "T5Tokenizer",
|
111 |
+
"unk_token": "<unk>"
|
112 |
+
}
|