arincon/ia-detection-deberta-v3-small

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- autextification2023
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: ia-detection-deberta-v3-small
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: autextification2023
+      type: autextification2023
+      config: detection_en
+      split: train
+      args: detection_en
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.6245419567607182
+    - name: F1
+      type: f1
+      value: 0.7308134379823322
+    - name: Precision
+      type: precision
+      value: 0.5776958621047713
+    - name: Recall
+      type: recall
+      value: 0.9943699731903485
+---
+
+<!-- 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. -->
+
+# ia-detection-deberta-v3-small
+
+This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the autextification2023 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.0506
+- Accuracy: 0.6245
+- F1: 0.7308
+- Precision: 0.5777
+- Recall: 0.9944
+
+## 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: 0.0001
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.2303        | 1.0   | 3808  | 0.3607          | 0.8984   | 0.8934 | 0.9231    | 0.8655 |
+| 0.1757        | 2.0   | 7616  | 0.5627          | 0.8606   | 0.8731 | 0.7903    | 0.9754 |
+| 0.0372        | 3.0   | 11424 | 0.4746          | 0.8978   | 0.9014 | 0.8575    | 0.9502 |
+| 0.1016        | 4.0   | 15232 | 0.6520          | 0.8910   | 0.8932 | 0.8620    | 0.9267 |
+| 0.0871        | 5.0   | 19040 | 0.7452          | 0.8730   | 0.8797 | 0.8235    | 0.9441 |
+| 0.0002        | 6.0   | 22848 | 0.7724          | 0.8942   | 0.8942 | 0.8802    | 0.9087 |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.13.3

added_tokens.json

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special_tokens_map.json

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tokenizer_config.json

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+  "model_max_length": 1000000000000000019884624838656,
+  "name_or_path": "microsoft/deberta-v3-small",
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+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
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+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}