add model

README.md

@@ -9,7 +9,7 @@ metrics:
 - recall
 - f1
 - accuracy
-model_index:
+model-index:
 - name: distilbert-base-uncased-finetuned-ingredients
   results:
   - task:
@@ -19,10 +19,19 @@ model_index:
       name: ingredients_yes_no
       type: ingredients_yes_no
       args: IngredientsYesNo
-    metric:
-      name: Accuracy
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9878658101356174
+    - name: Recall
+      type: recall
+      value: 0.9985569985569985
+    - name: F1
+      type: f1
+      value: 0.9931826336562611
+    - name: Accuracy
       type: accuracy
-      value: 0.9929481628108375
+      value: 0.9928244463689224
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,11 +41,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 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: 0.0262
-- Precision: 0.9914
+- Loss: 0.0280
+- Precision: 0.9879
 - Recall: 0.9986
-- F1: 0.9950
-- Accuracy: 0.9929
+- F1: 0.9932
+- Accuracy: 0.9928
 
 ## Model description
 
@@ -67,14 +76,14 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 186  | 0.0566          | 0.9814    | 0.9885 | 0.9849 | 0.9827   |
-| No log        | 2.0   | 372  | 0.0375          | 0.9864    | 0.9978 | 0.9921 | 0.9887   |
-| 0.113         | 3.0   | 558  | 0.0262          | 0.9914    | 0.9986 | 0.9950 | 0.9929   |
+| No log        | 1.0   | 186  | 0.0612          | 0.9807    | 0.9899 | 0.9853 | 0.9821   |
+| No log        | 2.0   | 372  | 0.0390          | 0.9836    | 0.9935 | 0.9885 | 0.9890   |
+| 0.1179        | 3.0   | 558  | 0.0280          | 0.9879    | 0.9986 | 0.9932 | 0.9928   |
 
 
 ### Framework versions
 
-- Transformers 4.9.2
+- Transformers 4.10.0
 - Pytorch 1.9.0+cu102
 - Datasets 1.11.0
 - Tokenizers 0.10.3

config.json

@@ -9,19 +9,19 @@
   "dropout": 0.1,
   "hidden_dim": 3072,
   "id2label": {
-    "0": "LABEL_0",
-    "1": "LABEL_1",
-    "2": "LABEL_2",
-    "3": "LABEL_3",
-    "4": "LABEL_4"
+    "0": "ADD-B",
+    "1": "ADD-C",
+    "2": "REM-B",
+    "3": "REM-C",
+    "4": "O"
   },
   "initializer_range": 0.02,
   "label2id": {
-    "LABEL_0": 0,
-    "LABEL_1": 1,
-    "LABEL_2": 2,
-    "LABEL_3": 3,
-    "LABEL_4": 4
+    "ADD-B": 0,
+    "ADD-C": 1,
+    "O": 4,
+    "REM-B": 2,
+    "REM-C": 3
   },
   "max_position_embeddings": 512,
   "model_type": "distilbert",
@@ -33,6 +33,6 @@
   "sinusoidal_pos_embds": false,
   "tie_weights_": true,
   "torch_dtype": "float32",
-  "transformers_version": "4.9.2",
+  "transformers_version": "4.10.0",
   "vocab_size": 30522
 }

pytorch_model.bin

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training_args.bin

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