Uploading food not food text classifier model

README.md

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+---
+license: apache-2.0
+base_model: distilbert/distilbert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: learn_hf_food_not_food_text_classifier-distilbert-base-uncased
+  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. -->
+
+# learn_hf_food_not_food_text_classifier-distilbert-base-uncased
+
+This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0009
+- Accuracy: 1.0
+
+## 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: 2e-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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.0129        | 1.0   | 7    | 0.0063          | 1.0      |
+| 0.006         | 2.0   | 14   | 0.0034          | 1.0      |
+| 0.0034        | 3.0   | 21   | 0.0022          | 1.0      |
+| 0.0025        | 4.0   | 28   | 0.0017          | 1.0      |
+| 0.002         | 5.0   | 35   | 0.0014          | 1.0      |
+| 0.0016        | 6.0   | 42   | 0.0012          | 1.0      |
+| 0.0015        | 7.0   | 49   | 0.0011          | 1.0      |
+| 0.0013        | 8.0   | 56   | 0.0010          | 1.0      |
+| 0.0013        | 9.0   | 63   | 0.0010          | 1.0      |
+| 0.0012        | 10.0  | 70   | 0.0009          | 1.0      |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "distilbert/distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "not_food",
+    "1": "food"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "food": 1,
+    "not_food": 0
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.2",
+  "vocab_size": 30522
+}

model.safetensors

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runs/Jun13_12-12-00_DESKTOP-TMAEDD2/events.out.tfevents.1718244727.DESKTOP-TMAEDD2

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special_tokens_map.json

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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
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+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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