Acc0.8764044943820225, F10.8761472718126238 , Augmented with roberta-base.csv, finetuned on google/electra-base-discriminator

README.md

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+---
+license: apache-2.0
+base_model: google/electra-base-discriminator
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: electra-base-discriminator_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. -->
+
+# electra-base-discriminator_roberta-base
+
+This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4180
+- Accuracy: 0.8768
+- F1: 0.8767
+- Precision: 0.8766
+- Recall: 0.8768
+
+## 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: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 1000
+- num_epochs: 25
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.955         | 1.0   | 91   | 0.8849          | 0.6349   | 0.5849 | 0.6173    | 0.6349 |
+| 0.4845        | 2.0   | 182  | 0.4777          | 0.8237   | 0.8221 | 0.8271    | 0.8237 |
+| 0.3434        | 3.0   | 273  | 0.3821          | 0.8580   | 0.8579 | 0.8598    | 0.8580 |
+| 0.2683        | 4.0   | 364  | 0.5158          | 0.8237   | 0.8213 | 0.8362    | 0.8237 |
+| 0.1675        | 5.0   | 455  | 0.3875          | 0.8643   | 0.8633 | 0.8651    | 0.8643 |
+| 0.1788        | 6.0   | 546  | 0.4180          | 0.8768   | 0.8767 | 0.8766    | 0.8768 |
+| 0.1669        | 7.0   | 637  | 0.4189          | 0.8768   | 0.8754 | 0.8775    | 0.8768 |
+| 0.1103        | 8.0   | 728  | 0.5338          | 0.8534   | 0.8542 | 0.8569    | 0.8534 |
+| 0.1597        | 9.0   | 819  | 0.4306          | 0.8674   | 0.8674 | 0.8676    | 0.8674 |
+| 0.1443        | 10.0  | 910  | 0.6446          | 0.8580   | 0.8574 | 0.8580    | 0.8580 |
+| 0.1012        | 11.0  | 1001 | 0.5104          | 0.8534   | 0.8535 | 0.8541    | 0.8534 |
+
+
+### Framework versions
+
+- Transformers 4.37.0
+- Pytorch 2.1.2
+- Datasets 2.1.0
+- Tokenizers 0.15.1

config.json

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+{
+  "_name_or_path": "google/electra-base-discriminator",
+  "architectures": [
+    "ElectraForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "embedding_size": 768,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "electra",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "summary_activation": "gelu",
+  "summary_last_dropout": 0.1,
+  "summary_type": "first",
+  "summary_use_proj": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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training_args.bin

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