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README.md

@@ -1,3 +1,79 @@
 ---
 license: mit
+tags:
+- generated_from_trainer
+datasets:
+- sagawa/ZINC-canonicalized
+metrics:
+- accuracy
+model-index:
+- name: ZINC-deberta-base-output
+  results:
+  - task:
+      name: Masked Language Modeling
+      type: fill-mask
+    dataset:
+      name: sagawa/ZINC-canonicalized
+      type: sagawa/ZINC-canonicalized
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9900059572833486
 ---
+
+<!-- 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. -->
+
+# ZINC-deberta-base-output
+
+This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the sagawa/ZINC-canonicalized dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0237
+- Accuracy: 0.9900
+
+## 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: 5e-05
+- train_batch_size: 20
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10.0
+
+### Training results
+
+| Training Loss | Epoch | Step   | Accuracy | Validation Loss |
+|:-------------:|:-----:|:------:|:--------:|:---------------:|
+| 0.045         | 1.06  | 100000 | 0.9842   | 0.0409          |
+| 0.0372        | 2.13  | 200000 | 0.9864   | 0.0346          |
+| 0.0337        | 3.19  | 300000 | 0.9874   | 0.0314          |
+| 0.0318        | 4.25  | 400000 | 0.9882   | 0.0293          |
+| 0.0296        | 5.31  | 500000 | 0.0277   | 0.9887          |
+| 0.0289        | 6.38  | 600000 | 0.0264   | 0.9891          |
+| 0.0267        | 7.44  | 700000 | 0.0253   | 0.9894          |
+| 0.0261        | 8.5   | 800000 | 0.0243   | 0.9898          |
+| 0.025         | 9.57  | 900000 | 0.0238   | 0.9900          |
+
+
+### Framework versions
+
+- Transformers 4.22.0.dev0
+- Pytorch 1.12.0
+- Datasets 2.4.1.dev0
+- Tokenizers 0.11.6

config.json

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+{
+  "_name_or_path": "microsoft/deberta-base",
+  "architectures": [
+    "NewDebertaForMaskedLM"
+  ],
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+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "c2p",
+    "p2c"
+  ],
+  "position_biased_input": false,
+  "relative_attention": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.22.0.dev0",
+  "type_vocab_size": 0,
+  "vocab_size": 298
+}

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pytorch_model.bin

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