End of training

README.md

@@ -23,12 +23,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value:
-        f1: 0.8172545518133599
+      value: 0.8199689183645938
     - name: Accuracy
       type: accuracy
-      value:
-        accuracy: 0.8328748280605227
+      value: 0.8370013755158184
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -38,9 +36,9 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the financial_phrasebank dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.7236
-- F1: {'f1': 0.8172545518133599}
-- Accuracy: {'accuracy': 0.8328748280605227}
+- Loss: 0.6190
+- F1: 0.8200
+- Accuracy: 0.8370
 
 ## Model description
 
@@ -69,17 +67,17 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | F1                         | Accuracy                         |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------:|:--------------------------------:|
-| 0.3941        | 0.94  | 100  | 0.4098          | {'f1': 0.7962755487135231} | {'accuracy': 0.828060522696011}  |
-| 0.1921        | 1.89  | 200  | 0.5360          | {'f1': 0.8094154455783058} | {'accuracy': 0.8321870701513068} |
-| 0.0873        | 2.83  | 300  | 0.7086          | {'f1': 0.8146311198809535} | {'accuracy': 0.8301237964236589} |
-| 0.0404        | 3.77  | 400  | 0.7236          | {'f1': 0.8172545518133599} | {'accuracy': 0.8328748280605227} |
+| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
+| 0.6472        | 0.94  | 100  | 0.5388          | 0.7457 | 0.7558   |
+| 0.2832        | 1.89  | 200  | 0.4992          | 0.8072 | 0.8177   |
+| 0.1671        | 2.83  | 300  | 0.5158          | 0.8190 | 0.8377   |
+| 0.0834        | 3.77  | 400  | 0.6190          | 0.8200 | 0.8370   |
 
 
 ### Framework versions
 
-- Transformers 4.33.2
-- Pytorch 2.0.1+cu118
-- Datasets 2.14.5
-- Tokenizers 0.13.3
+- Transformers 4.34.1
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.14.1

config.json

@@ -10,16 +10,16 @@
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
   "id2label": {
-    "0": "negative",
-    "1": "neutral",
-    "2": "positive"
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
-    "negative": 0,
-    "neutral": 1,
-    "positive": 2
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
   },
   "layer_norm_eps": 1e-12,
   "max_position_embeddings": 512,
@@ -30,7 +30,7 @@
   "position_embedding_type": "absolute",
   "problem_type": "single_label_classification",
   "torch_dtype": "float32",
-  "transformers_version": "4.33.2",
+  "transformers_version": "4.34.1",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 30522

pytorch_model.bin

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+size 438007022

tokenizer_config.json

@@ -1,4 +1,46 @@
 {
+  "added_tokens_decoder": {
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+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "single_word": false,
+      "special": true
+    }
+  },
   "clean_up_tokenization_spaces": true,
   "cls_token": "[CLS]",
   "do_lower_case": true,

training_args.bin

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