End of training

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: distilbert-base-uncased-rvl-cdip
+  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. -->
+
+# distilbert-base-uncased-rvl-cdip
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.2055
+- Accuracy: 0.7079
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 15
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 48   | 2.4392          | 0.45     |
+| No log        | 2.0   | 96   | 1.5529          | 0.5632   |
+| No log        | 3.0   | 144  | 1.3164          | 0.6132   |
+| No log        | 4.0   | 192  | 1.1269          | 0.6395   |
+| No log        | 5.0   | 240  | 1.0145          | 0.7      |
+| No log        | 6.0   | 288  | 1.0839          | 0.6816   |
+| No log        | 7.0   | 336  | 1.1414          | 0.6868   |
+| No log        | 8.0   | 384  | 1.1220          | 0.7053   |
+| No log        | 9.0   | 432  | 1.1402          | 0.7105   |
+| No log        | 10.0  | 480  | 1.1805          | 0.7132   |
+| 0.8154        | 11.0  | 528  | 1.1923          | 0.7132   |
+| 0.8154        | 12.0  | 576  | 1.2007          | 0.7079   |
+| 0.8154        | 13.0  | 624  | 1.1973          | 0.7079   |
+| 0.8154        | 14.0  | 672  | 1.2049          | 0.7105   |
+| 0.8154        | 15.0  | 720  | 1.2055          | 0.7079   |
+
+
+### Framework versions
+
+- Transformers 4.36.0
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0

config.json

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+{
+  "_name_or_path": "distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "presentation",
+    "1": "budget",
+    "2": "scientific publication",
+    "3": "letter",
+    "4": "invoice",
+    "5": "handwritten",
+    "6": "email",
+    "7": "questionnaire",
+    "8": "memo",
+    "9": "specification",
+    "10": "resume",
+    "11": "file folder",
+    "12": "form",
+    "13": "scientific report",
+    "14": "news article",
+    "15": "advertisement"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "advertisement": 15,
+    "budget": 1,
+    "email": 6,
+    "file folder": 11,
+    "form": 12,
+    "handwritten": 5,
+    "invoice": 4,
+    "letter": 3,
+    "memo": 8,
+    "news article": 14,
+    "presentation": 0,
+    "questionnaire": 7,
+    "resume": 10,
+    "scientific publication": 2,
+    "scientific report": 13,
+    "specification": 9
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.0",
+  "vocab_size": 30522
+}

model.safetensors

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runs/Dec14_03-25-07_0b4147b7eda3/events.out.tfevents.1702524313.0b4147b7eda3.5564.0

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special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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