jordan2889/scenario_4

README.md

@@ -1,199 +1,88 @@
 ---
-library_name: transformers
-tags: []
+license: mit
+base_model: microsoft/mdeberta-v3-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: scenario_4
+  results: []
 ---
 
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+<!-- 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. -->
+
+# scenario_4
+
+This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1764
+- Accuracy: 0.9704
+- F1: 0.9704
+- Precision: 0.9710
+- Recall: 0.9704
+- Accuracy Label Test: 0.9879
+- Accuracy Label Train: 0.9536
+
+## 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Accuracy Label Test | Accuracy Label Train |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------------:|:--------------------:|
+| 0.5515        | 0.1579 | 100  | 0.5194          | 0.7579   | 0.7415 | 0.8352    | 0.7579 | 0.5070              | 0.9992               |
+| 0.2205        | 0.3157 | 200  | 0.2537          | 0.9300   | 0.9298 | 0.9361    | 0.9300 | 0.9883              | 0.8739               |
+| 0.1106        | 0.4736 | 300  | 0.3450          | 0.9129   | 0.9124 | 0.9248    | 0.9129 | 0.9960              | 0.8329               |
+| 0.0384        | 0.6314 | 400  | 0.1408          | 0.9683   | 0.9683 | 0.9687    | 0.9683 | 0.9835              | 0.9536               |
+| 0.0631        | 0.7893 | 500  | 0.1517          | 0.9631   | 0.9631 | 0.9645    | 0.9631 | 0.9895              | 0.9377               |
+| 0.0276        | 0.9471 | 600  | 0.3649          | 0.9387   | 0.9386 | 0.9444    | 0.9387 | 0.9948              | 0.8847               |
+| 0.0245        | 1.1050 | 700  | 0.1339          | 0.9702   | 0.9702 | 0.9702    | 0.9702 | 0.9727              | 0.9679               |
+| 0.0519        | 1.2628 | 800  | 0.4945          | 0.9186   | 0.9182 | 0.9299    | 0.9186 | 0.9992              | 0.8410               |
+| 0.02          | 1.4207 | 900  | 0.2637          | 0.9549   | 0.9548 | 0.9580    | 0.9549 | 0.9960              | 0.9153               |
+| 0.0325        | 1.5785 | 1000 | 0.1165          | 0.9708   | 0.9708 | 0.9712    | 0.9708 | 0.9851              | 0.9571               |
+| 0.016         | 1.7364 | 1100 | 0.1007          | 0.9692   | 0.9692 | 0.9697    | 0.9692 | 0.9530              | 0.9849               |
+| 0.0068        | 1.8942 | 1200 | 0.1679          | 0.9690   | 0.9690 | 0.9697    | 0.9690 | 0.9871              | 0.9516               |
+| 0.0042        | 2.0521 | 1300 | 0.1182          | 0.9734   | 0.9734 | 0.9734    | 0.9734 | 0.9723              | 0.9745               |
+| 0.0005        | 2.2099 | 1400 | 0.1432          | 0.9730   | 0.9730 | 0.9731    | 0.9730 | 0.9799              | 0.9663               |
+| 0.0182        | 2.3678 | 1500 | 0.1460          | 0.9718   | 0.9718 | 0.9723    | 0.9718 | 0.9871              | 0.9571               |
+| 0.0004        | 2.5257 | 1600 | 0.1383          | 0.9732   | 0.9732 | 0.9734    | 0.9732 | 0.9843              | 0.9625               |
+| 0.0003        | 2.6835 | 1700 | 0.1381          | 0.9744   | 0.9744 | 0.9745    | 0.9744 | 0.9831              | 0.9660               |
+| 0.0002        | 2.8414 | 1800 | 0.1599          | 0.9724   | 0.9724 | 0.9728    | 0.9724 | 0.9863              | 0.9590               |
+
+
+### Framework versions
+
+- Transformers 4.44.0
+- Pytorch 2.3.1
+- Datasets 2.20.0
+- Tokenizers 0.19.1

config.json

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