Acc0.8370786516853933, F10.8383201201124987 , Augmented with flang-bert.csv, finetuned on SALT-NLP/FLANG-ELECTRA

README.md

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+---
+base_model: SALT-NLP/FLANG-ELECTRA
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: FLANG-ELECTRA_flang-bert
+  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. -->
+
+# FLANG-ELECTRA_flang-bert
+
+This model is a fine-tuned version of [SALT-NLP/FLANG-ELECTRA](https://huggingface.co/SALT-NLP/FLANG-ELECTRA) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5930
+- Accuracy: 0.8705
+- F1: 0.8717
+- Precision: 0.8772
+- Recall: 0.8705
+
+## 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: 32
+- eval_batch_size: 32
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.6377        | 1.0   | 181  | 0.5174          | 0.8003   | 0.7860 | 0.8080    | 0.8003 |
+| 0.4035        | 2.0   | 362  | 0.4221          | 0.8580   | 0.8578 | 0.8611    | 0.8580 |
+| 0.2395        | 3.0   | 543  | 0.4535          | 0.8580   | 0.8560 | 0.8592    | 0.8580 |
+| 0.231         | 4.0   | 724  | 0.4335          | 0.8658   | 0.8657 | 0.8659    | 0.8658 |
+| 0.3369        | 5.0   | 905  | 0.5608          | 0.8081   | 0.8057 | 0.8151    | 0.8081 |
+| 0.2203        | 6.0   | 1086 | 0.5002          | 0.8705   | 0.8691 | 0.8706    | 0.8705 |
+| 0.239         | 7.0   | 1267 | 0.6676          | 0.8128   | 0.8125 | 0.8338    | 0.8128 |
+| 0.0938        | 8.0   | 1448 | 0.5930          | 0.8705   | 0.8717 | 0.8772    | 0.8705 |
+| 0.1329        | 9.0   | 1629 | 0.5017          | 0.8580   | 0.8571 | 0.8572    | 0.8580 |
+| 0.3598        | 10.0  | 1810 | 0.5126          | 0.8690   | 0.8675 | 0.8698    | 0.8690 |
+| 0.1615        | 11.0  | 1991 | 0.5945          | 0.8612   | 0.8605 | 0.8606    | 0.8612 |
+| 0.0923        | 12.0  | 2172 | 0.8213          | 0.8268   | 0.8292 | 0.8450    | 0.8268 |
+| 0.1296        | 13.0  | 2353 | 0.8647          | 0.8580   | 0.8586 | 0.8611    | 0.8580 |
+
+
+### 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": "SALT-NLP/FLANG-ELECTRA",
+  "architectures": [
+    "ElectraForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "embedding_size": 1024,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "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": 16,
+  "num_hidden_layers": 24,
+  "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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+size 1340628036

training_args.bin

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