wyu97
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Browse files- README.md +30 -1
- config.json +51 -0
- tokenizer.json +0 -0
README.md
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license: cc-by-
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---
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license: cc-by-4.0
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---
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# GenRead (MergeDPR): FiD model trained on NQ
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-- This is the model checkpoint of GenRead [2], based on the T5-3B and trained on the NQ dataset [1].
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-- Hyperparameters: 8 x 80GB A100 GPUs; batch size 16; AdamW; LR 3e-5; best dev at 19000 steps.
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References:
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[1] Natural Questions: A Benchmark for Question Answering Research. TACL 2019.
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[2] Generate rather than Retrieve: Large Language Models are Strong Context Generators. arXiv 2022
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## Model performance
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We evaluate it on the TriviaQA dataset, the EM score is 54.38.
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<a href="https://huggingface.co/exbert/?model=bert-base-uncased">
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<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
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</a>
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---
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license: cc-by-4.0
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---
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---
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license: cc-by-4.0
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---
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config.json
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{
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"architectures": [
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"FiDT5"
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],
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"d_ff": 16384,
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"d_kv": 128,
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"d_model": 1024,
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"decoder_start_token_id": 0,
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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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_heads": 32,
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"num_layers": 24,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_num_buckets": 32,
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"task_specific_params": {
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"summarization": {
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"early_stopping": true,
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"length_penalty": 2.0,
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"max_length": 200,
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"min_length": 30,
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"no_repeat_ngram_size": 3,
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"num_beams": 4,
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"prefix": "summarize: "
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},
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"translation_en_to_de": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to German: "
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},
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"translation_en_to_fr": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to French: "
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},
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"translation_en_to_ro": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to Romanian: "
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}
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},
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"vocab_size": 32128
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}
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tokenizer.json
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