Initial Commit

README.md

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+---
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+datasets:
+- hate_speech_filipino
+metrics:
+- accuracy
+- f1
+model-index:
+- name: scenario-teacher-data-hate_speech_filipino-model-xlm-roberta-base
+  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. -->
+
+# scenario-teacher-data-hate_speech_filipino-model-xlm-roberta-base
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the hate_speech_filipino dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0437
+- Accuracy: 0.7817
+- F1: 0.7687
+
+## 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: linear
+- num_epochs: 6969
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| No log        | 0.32  | 100  | 0.5923          | 0.6966   | 0.7200 |
+| No log        | 0.64  | 200  | 0.5214          | 0.7450   | 0.7202 |
+| No log        | 0.96  | 300  | 0.5052          | 0.7554   | 0.7372 |
+| No log        | 1.28  | 400  | 0.5106          | 0.7649   | 0.7442 |
+| 0.5444        | 1.6   | 500  | 0.5499          | 0.7559   | 0.7564 |
+| 0.5444        | 1.92  | 600  | 0.4998          | 0.7566   | 0.6862 |
+| 0.5444        | 2.24  | 700  | 0.5269          | 0.7760   | 0.7653 |
+| 0.5444        | 2.56  | 800  | 0.5129          | 0.7836   | 0.7716 |
+| 0.5444        | 2.88  | 900  | 0.5132          | 0.7668   | 0.7070 |
+| 0.3971        | 3.19  | 1000 | 0.5680          | 0.7805   | 0.7510 |
+| 0.3971        | 3.51  | 1100 | 0.5999          | 0.7781   | 0.7696 |
+| 0.3971        | 3.83  | 1200 | 0.6097          | 0.7632   | 0.7674 |
+| 0.3971        | 4.15  | 1300 | 0.6476          | 0.7795   | 0.7573 |
+| 0.3971        | 4.47  | 1400 | 0.6461          | 0.7843   | 0.7629 |
+| 0.2704        | 4.79  | 1500 | 0.6329          | 0.7786   | 0.7634 |
+| 0.2704        | 5.11  | 1600 | 0.7783          | 0.7729   | 0.7396 |
+| 0.2704        | 5.43  | 1700 | 0.6963          | 0.7750   | 0.7285 |
+| 0.2704        | 5.75  | 1800 | 0.7857          | 0.7892   | 0.7680 |
+| 0.2704        | 6.07  | 1900 | 0.6921          | 0.7762   | 0.7655 |
+| 0.215         | 6.39  | 2000 | 0.7196          | 0.7722   | 0.7499 |
+| 0.215         | 6.71  | 2100 | 1.0259          | 0.7691   | 0.7671 |
+| 0.215         | 7.03  | 2200 | 1.1496          | 0.7767   | 0.7640 |
+| 0.215         | 7.35  | 2300 | 1.0437          | 0.7817   | 0.7687 |
+
+
+### Framework versions
+
+- Transformers 4.33.3
+- Pytorch 2.0.1
+- Datasets 2.14.5
+- Tokenizers 0.13.3

config.json

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+{
+  "_name_or_path": "xlm-roberta-base",
+  "architectures": [
+    "XLMRobertaForSequenceClassificationKD"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

pytorch_model.bin

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training_args.bin

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