add model

.gitignore

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+checkpoint-*/

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- ingredients_yes_no
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model_index:
+- name: distilbert-base-uncased-finetuned-ingredients
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: ingredients_yes_no
+      type: ingredients_yes_no
+      args: IngredientsYesNo
+    metric:
+      name: Accuracy
+      type: accuracy
+      value: 0.9918144611186903
+---
+
+<!-- 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. -->
+
+# distilbert-base-uncased-finetuned-ingredients
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ingredients_yes_no dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0264
+- Precision: 0.9926
+- Recall: 1.0
+- F1: 0.9963
+- Accuracy: 0.9918
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 170  | 0.0584          | 0.9739    | 0.9942 | 0.9839 | 0.9804   |
+| No log        | 2.0   | 340  | 0.0423          | 0.9861    | 1.0    | 0.9930 | 0.9870   |
+| 0.123         | 3.0   | 510  | 0.0264          | 0.9926    | 1.0    | 0.9963 | 0.9918   |
+
+
+### Framework versions
+
+- Transformers 4.9.2
+- Pytorch 1.9.0+cu102
+- Datasets 1.11.0
+- Tokenizers 0.10.3

config.json

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+{
+  "_name_or_path": "distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForTokenClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.9.2",
+  "vocab_size": 30522
+}

pytorch_model.bin

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special_tokens_map.json

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tokenizer_config.json

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training_args.bin

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