forthisdream/ATSI-text-classification-model

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: TTC4900Model
+  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. -->
+
+# TTC4900Model
+
+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: 0.0667
+- Accuracy: 0.9859
+- F1: 0.9418
+- Precision: 0.9562
+- Recall: 0.9309
+
+## 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: 16
+- 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: 100
+- num_epochs: 3
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 1.5192        | 0.3289 | 50   | 0.9342          | 0.7575   | 0.1077 | 0.0947    | 0.125  |
+| 0.6007        | 0.6579 | 100  | 0.4256          | 0.8767   | 0.3189 | 0.2983    | 0.3445 |
+| 0.2704        | 0.9868 | 150  | 0.2471          | 0.9561   | 0.6877 | 0.6916    | 0.6917 |
+| 0.1382        | 1.3158 | 200  | 0.1346          | 0.9727   | 0.8789 | 0.9054    | 0.8698 |
+| 0.1132        | 1.6447 | 250  | 0.0824          | 0.9876   | 0.9350 | 0.9701    | 0.9103 |
+| 0.0981        | 1.9737 | 300  | 0.0431          | 0.9942   | 0.9749 | 0.9892    | 0.9635 |
+| 0.0369        | 2.3026 | 350  | 0.0466          | 0.9892   | 0.9376 | 0.9576    | 0.9275 |
+| 0.0373        | 2.6316 | 400  | 0.0413          | 0.9909   | 0.9602 | 0.9580    | 0.9630 |
+| 0.0235        | 2.9605 | 450  | 0.0407          | 0.9909   | 0.9613 | 0.9600    | 0.9630 |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Tokenizers 0.19.1

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": "atis_airfare",
+    "1": "atis_flight",
+    "2": "atis_abbreviation",
+    "3": "atis_ground_service",
+    "4": "atis_flight_time",
+    "5": "atis_quantity",
+    "6": "atis_airline",
+    "7": "atis_aircraft"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "atis_abbreviation": 2,
+    "atis_aircraft": 7,
+    "atis_airfare": 0,
+    "atis_airline": 6,
+    "atis_flight": 1,
+    "atis_flight_time": 4,
+    "atis_ground_service": 3,
+    "atis_quantity": 5
+  },
+  "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": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+size 437977104

training_args.bin

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