End of training

README.md

@@ -15,9 +15,9 @@ 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 None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5490
-- F1-score: {0: 0, 1: 0.67, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0.33, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0.33, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0}
-- Marco-avg-f1-score: 0.04
+- Loss: 0.5633
+- F1-score: {'Self-direction: thought attained': 0.67, 'Self-direction: thought constrained': 0, 'Self-direction: action attained': 0, 'Self-direction: action constrained': 0, 'Stimulation attained': 0, 'Stimulation constrained': 0, 'Hedonism attained': 0, 'Hedonism constrained': 0, 'Achievement attained': 0.33, 'Achievement constrained': 0, 'Power: dominance attained': 0, 'Power: dominance constrained': 0, 'Power: resources attained': 0, 'Power: resources constrained': 0, 'Face attained': 0, 'Face constrained': 0, 'Security: personal attained': 0, 'Security: personal constrained': 0, 'Security: societal attained': 0, 'Security: societal constrained': 0, 'Tradition attained': 0, 'Tradition constrained': 0, 'Conformity: rules attained': 0, 'Conformity: rules constrained': 0, 'Conformity: interpersonal attained': 0.57, 'Conformity: interpersonal constrained': 0, 'Humility attained': 0, 'Humility constrained': 0, 'Benevolence: caring attained': 0, 'Benevolence: caring constrained': 0, 'Benevolence: dependability attained': 0, 'Benevolence: dependability constrained': 1.0, 'Universalism: concern attained': 0, 'Universalism: concern constrained': 0, 'Universalism: nature attained': 0, 'Universalism: nature constrained': 0, 'Universalism: tolerance attained': 0, 'Universalism: tolerance constrained': 0}
+- Marco-avg-f1-score: 0.07
 
 ## Model description
 
@@ -50,7 +50,7 @@ The following hyperparameters were used during training:
 
 ### Framework versions
 
-- Transformers 4.38.0
-- Pytorch 2.2.0+cu121
-- Datasets 2.17.1
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
 - Tokenizers 0.15.2

config.json

@@ -100,7 +100,7 @@
   "position_embedding_type": "absolute",
   "problem_type": "multi_label_classification",
   "torch_dtype": "float32",
-  "transformers_version": "4.38.0",
+  "transformers_version": "4.38.2",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 30522

model.safetensors

@@ -1,3 +1,3 @@
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training_args.bin

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