Model save

README.md

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+---
+library_name: transformers
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: profesor_MViT_B_VIOPERU
+  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. -->
+
+# profesor_MViT_B_VIOPERU
+
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3806
+- Accuracy: 0.8929
+- F1: 0.8916
+- Precision: 0.9118
+- Recall: 0.8929
+- Roc Auc: 0.9579
+
+## 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: 1e-05
+- train_batch_size: 22
+- eval_batch_size: 22
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 90
+- training_steps: 900
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Roc Auc |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
+| 0.6576        | 7.0067  | 90   | 0.6648          | 0.6786   | 0.6719 | 0.6944    | 0.6786 | 0.7857  |
+| 0.5634        | 14.0133 | 180  | 0.5872          | 0.75     | 0.7497 | 0.7513    | 0.75   | 0.8533  |
+| 0.4258        | 22.0067 | 270  | 0.4743          | 0.875    | 0.8750 | 0.8755    | 0.875  | 0.9133  |
+| 0.3059        | 29.0133 | 360  | 0.4014          | 0.8393   | 0.8392 | 0.8397    | 0.8393 | 0.9298  |
+| 0.2045        | 37.0067 | 450  | 0.3394          | 0.8571   | 0.8570 | 0.8590    | 0.8571 | 0.9401  |
+| 0.1448        | 44.0133 | 540  | 0.3734          | 0.8393   | 0.8392 | 0.8397    | 0.8393 | 0.9279  |
+| 0.1082        | 52.0067 | 630  | 0.3368          | 0.8929   | 0.8916 | 0.9118    | 0.8929 | 0.9515  |
+| 0.0912        | 59.0133 | 720  | 0.3935          | 0.8571   | 0.8564 | 0.8646    | 0.8571 | 0.9388  |
+| 0.0736        | 67.0067 | 810  | 0.3789          | 0.8929   | 0.8916 | 0.9118    | 0.8929 | 0.9522  |
+| 0.0615        | 74.0133 | 900  | 0.3806          | 0.8929   | 0.8916 | 0.9118    | 0.8929 | 0.9579  |
+
+
+### Framework versions
+
+- Transformers 4.46.1
+- Pytorch 2.0.1+cu118
+- Datasets 3.1.0
+- Tokenizers 0.20.1

all_results.json

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+{
+    "epoch": 74.01333333333334,
+    "total_flos": 0.0,
+    "train_loss": 0.2784769135051303,
+    "train_runtime": 11488.6132,
+    "train_samples_per_second": 1.723,
+    "train_steps_per_second": 0.078
+}

model.safetensors

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-oid sha256:b293b0ae02ccb9ea4c24887315fdd153bf1c629262cc8a1815c9b066a4c4e390
+oid sha256:ef517c2112cf1b10ebde32bf3b09172510e777126da501da2f605754396af5e0
 size 137270032

train_results.json

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+{
+    "epoch": 74.01333333333334,
+    "total_flos": 0.0,
+    "train_loss": 0.2784769135051303,
+    "train_runtime": 11488.6132,
+    "train_samples_per_second": 1.723,
+    "train_steps_per_second": 0.078
+}