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Model: NasimB/children_bnc_rarity_all_no_cut
Source: Original Platform

.gitattributes

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.gitignore

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+checkpoint-*/

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- generator
+model-index:
+- name: children_bnc_rarity_all_no_cut
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# children_bnc_rarity_all_no_cut
+
+This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
+It achieves the following results on the evaluation set:
+- Loss: 4.3266
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0005
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 1000
+- num_epochs: 6
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss |
+|:-------------:|:-----:|:-----:|:---------------:|
+| 6.7047        | 0.29  | 500   | 5.6398          |
+| 5.3501        | 0.58  | 1000  | 5.2066          |
+| 5.0056        | 0.88  | 1500  | 4.9588          |
+| 4.7258        | 1.17  | 2000  | 4.8173          |
+| 4.5734        | 1.46  | 2500  | 4.6948          |
+| 4.4663        | 1.75  | 3000  | 4.5804          |
+| 4.3402        | 2.05  | 3500  | 4.5071          |
+| 4.1471        | 2.34  | 4000  | 4.4576          |
+| 4.1137        | 2.63  | 4500  | 4.4027          |
+| 4.0777        | 2.92  | 5000  | 4.3468          |
+| 3.8629        | 3.22  | 5500  | 4.3449          |
+| 3.8078        | 3.51  | 6000  | 4.3108          |
+| 3.8044        | 3.8   | 6500  | 4.2763          |
+| 3.7029        | 4.09  | 7000  | 4.2803          |
+| 3.5324        | 4.39  | 7500  | 4.2741          |
+| 3.5239        | 4.68  | 8000  | 4.2585          |
+| 3.5091        | 4.97  | 8500  | 4.2454          |
+| 3.3521        | 5.26  | 9000  | 4.2592          |
+| 3.3357        | 5.56  | 9500  | 4.2584          |
+| 3.3348        | 5.85  | 10000 | 4.2573          |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 1.11.0+cu113
+- Datasets 2.13.0
+- Tokenizers 0.13.3

config.json

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+{
+  "_name_or_path": "gpt2",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 128,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.1",
+  "use_cache": true,
+  "vocab_size": 50257
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "4.26.1"
+}

pytorch_model.bin

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special_tokens_map.json

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+{
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "pad_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 1024,
+  "name_or_path": "gpt2",
+  "special_tokens_map_file": null,
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}

training_args.bin

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