End of training

.gitignore

@@ -0,0 +1 @@
+checkpoint-*/

README.md

@@ -1,58 +1,63 @@
 ---
 license: mit
+tags:
+- generated_from_trainer
+model-index:
+- name: donut_finetuned_chart
+  results: []
 ---
-### Usage
-Inference Code for this model
-
-```
-import re
-import transformers
-from transformers import DonutProcessor, VisionEncoderDecoderModel
-import torch
-
-fine_tuned_model = VisionEncoderDecoderModel.from_pretrained("aravind-selvam/donut_finetuned_chart")
-processor = DonutProcessor.from_pretrained("aravind-selvam/donut_finetuned_chart")
-
-# Move model to GPU
-device = "cuda" if torch.cuda.is_available() else "cpu"
-fine_tuned_model.to(device)
-
-# Load random document image from the test set
-dataset = load_dataset("hf-internal-testing/example-documents", split="test")
-sample_image = dataset[1]
-
-def run_prediction(sample, model=fine_tuned_model, processor=processor):
-    # pixel values
-    pixel_values = processor(image, return_tensors="pt").pixel_values
-    # prepare inputs
-    task_prompt = "<s>"
-    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
-
-    # run inference
-    outputs = model.generate(
-        pixel_values.to(device),
-        decoder_input_ids=decoder_input_ids.to(device),
-        max_length=model.decoder.config.max_position_embeddings,
-        early_stopping=True,
-        pad_token_id=processor.tokenizer.pad_token_id,
-        eos_token_id=processor.tokenizer.eos_token_id,
-        use_cache=True,
-        num_beams=2,
-        # bad_words_ids=[[processor.tokenizer.unk_token_id]],
-        return_dict_in_generate=True,
-    )
-
-    # process output
-    prediction = processor.batch_decode(outputs.sequences)[0]
-    prediction = re.sub(r"<one>", "1", prediction)
-    prediction = processor.token2json(prediction)
-
-
-    # load reference target
-    target = processor.token2json(test_sample["target_sequence"])
-    return prediction, target
-
-prediction, target = run_prediction(sample_image)
-print(f"Reference:\n {target}")
-print(f"Prediction:\n {prediction}")
-```
+
+<!-- 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. -->
+
+# donut_finetuned_chart
+
+This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4957
+- Cer: 0.2318
+
+## 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: 2.3e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 3
+- total_train_batch_size: 24
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 4
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Cer    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 3.4943        | 1.0   | 166  | 0.6634          | 0.2341 |
+| 0.475         | 2.0   | 333  | 0.5370          | 0.2320 |
+| 0.3009        | 3.0   | 500  | 0.5051          | 0.2318 |
+| 0.2611        | 3.98  | 664  | 0.4957          | 0.2318 |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.0+cu118
+- Datasets 2.11.0
+- Tokenizers 0.13.3

config.json

@@ -1,6 +1,6 @@
 {
-  "_commit_hash": null,
-  "_name_or_path": "/content/aravind-selvam/donut_finetuned_chart_crop/checkpoint-800",
+  "_commit_hash": "a959cf33c20e09215873e338299c900f57047c61",
+  "_name_or_path": "naver-clova-ix/donut-base",
   "architectures": [
     "VisionEncoderDecoderModel"
   ],
@@ -50,7 +50,7 @@
       "LABEL_1": 1
     },
     "length_penalty": 1.0,
-    "max_length": 179,
+    "max_length": 198,
     "max_position_embeddings": 1536,
     "min_length": 0,
     "model_type": "mbart",
@@ -126,8 +126,8 @@
       "1": "LABEL_1"
     },
     "image_size": [
-      800,
-      800
+      512,
+      512
     ],
     "initializer_range": 0.02,
     "is_decoder": false,

generation_config.json

@@ -3,7 +3,7 @@
   "decoder_start_token_id": 0,
   "eos_token_id": 2,
   "forced_eos_token_id": 2,
-  "max_length": 179,
+  "max_length": 198,
   "pad_token_id": 1,
   "transformers_version": "4.28.1"
 }

preprocessor_config.json

@@ -19,8 +19,8 @@
   "processor_class": "DonutProcessor",
   "resample": 2,
   "rescale_factor": 0.00392156862745098,
-  "size": {
-    "height": 800,
-    "width": 800
-  }
+  "size": [
+    512,
+    512
+  ]
 }

pytorch_model.bin

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+size 809228057

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training_args.bin

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