End of training

README.md

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+---
+license: apache-2.0
+base_model: bert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: AugWordNet_BERT_FPB_finetuned
+  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. -->
+
+# AugWordNet_BERT_FPB_finetuned
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3789
+- Accuracy: 0.9097
+- F1: 0.9100
+- Precision: 0.9140
+- Recall: 0.9097
+
+## 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.8426        | 1.0   | 91   | 0.7693          | 0.6978   | 0.6777 | 0.6887    | 0.6978 |
+| 0.4269        | 2.0   | 182  | 0.3264          | 0.8816   | 0.8803 | 0.8820    | 0.8816 |
+| 0.3055        | 3.0   | 273  | 0.2990          | 0.8832   | 0.8838 | 0.8888    | 0.8832 |
+| 0.2135        | 4.0   | 364  | 0.3049          | 0.9003   | 0.8998 | 0.9006    | 0.9003 |
+| 0.1275        | 5.0   | 455  | 0.3764          | 0.8801   | 0.8786 | 0.8839    | 0.8801 |
+| 0.1033        | 6.0   | 546  | 0.3393          | 0.9019   | 0.9007 | 0.9048    | 0.9019 |
+| 0.0635        | 7.0   | 637  | 0.3829          | 0.9081   | 0.9079 | 0.9082    | 0.9081 |
+| 0.0657        | 8.0   | 728  | 0.4759          | 0.8972   | 0.8958 | 0.8986    | 0.8972 |
+| 0.0548        | 9.0   | 819  | 0.3789          | 0.9097   | 0.9100 | 0.9140    | 0.9097 |
+| 0.0695        | 10.0  | 910  | 0.4797          | 0.8894   | 0.8876 | 0.8979    | 0.8894 |
+
+
+### 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": "bert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "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": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "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 437961724

training_args.bin

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