Saving weights of epoch 2 at step 5
Browse files
config.json
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{
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"activation_function": "gelu_new",
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"architectures": [
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"GPTNeoForMultipleChoice"
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],
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"attention_dropout": 0,
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"attention_layers": [
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local"
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],
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"attention_types": [
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[
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[
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"global",
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"local"
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],
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12
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]
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],
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"bos_token_id": 50256,
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"embed_dropout": 0,
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"eos_token_id": 50256,
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"gradient_checkpointing": false,
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": null,
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"layer_norm_epsilon": 1e-05,
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"max_position_embeddings": 2048,
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"model_type": "gpt_neo",
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"num_heads": 16,
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"num_layers": 24,
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"resid_dropout": 0,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"task_specific_params": {
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"text-generation": {
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"do_sample": true,
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"max_length": 50,
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"temperature": 0.9
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}
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},
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"tokenizer_class": "GPT2Tokenizer",
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"transformers_version": "4.10.0.dev0",
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"use_cache": true,
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"vocab_size": 50257,
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"window_size": 256
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}
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flax_model.msgpack
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version https://git-lfs.github.com/spec/v1
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oid sha256:026a9da1385727ad25b118be45961965cf77faaf56bfa6193d1b1105fd73fc2c
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size 5262347427
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results_tensorboard/events.out.tfevents.1627575327.t1v-n-cb40e504-w-0.12731.3.v2
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version https://git-lfs.github.com/spec/v1
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oid sha256:8da59be43f840d393332e6be49c905a9a5e49e95cfb3aa2d172593aa454e3283
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size 1160
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src/__pycache__/model_file.cpython-38.pyc
ADDED
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Binary file (9.25 kB). View file
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src/gptneo_story.py
CHANGED
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@@ -44,8 +44,8 @@ def preprocess(example):
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train_dataset=train_dataset.map(preprocess)
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validation_dataset=validation_dataset.map(preprocess)
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-
len_train_dataset=
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len_validation_dataset=
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train_dataset=train_dataset.select(range(len_train_dataset))
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validation_dataset=validation_dataset.select(range(len_validation_dataset))
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train_dataset=train_dataset.map(preprocess)
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validation_dataset=validation_dataset.map(preprocess)
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len_train_dataset=100
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len_validation_dataset=100
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train_dataset=train_dataset.select(range(len_train_dataset))
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validation_dataset=validation_dataset.select(range(len_validation_dataset))
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