update C1 to have all the labels being computed over schema

runs/base_models/mbert/jbcs2025_mbert_base-C1-encoder_classification-C1/.hydra/hydra.yaml

@@ -140,7 +140,7 @@ hydra:
     - path: ''
       schema: structured
       provider: schema
-    output_dir: /workspace/jbcs2025/outputs/2025-05-25/20-13-20
+    output_dir: /workspace/jbcs2025/outputs/2025-05-25/21-44-30
     choices:
       experiments: base_models/C1
       hydra/env: default

runs/base_models/mbert/jbcs2025_mbert_base-C1-encoder_classification-C1/evaluation_results.csv

@@ -1,2 +1,2 @@
-accuracy,RMSE,QWK,HDIV,Macro_F1,Micro_F1,Weighted_F1,TP_0,TN_0,FP_0,FN_0,TP_1,TN_1,FP_1,FN_1,TP_2,TN_2,FP_2,FN_2,TP_3,TN_3,FP_3,FN_3,TP_4,TN_4,FP_4,FN_4,timestamp,id
-0.5362318840579711,30.072376462244492,0.4505920783993467,0.007246376811594235,0.3244639912039582,0.5362318840579711,0.518137852459147,0,137,0,1,5,123,5,5,42,44,28,24,27,58,29,24,0,126,2,10,2025-05-25 20:13:20,jbcs2025_mbert_base-C1-encoder_classification-C1
+accuracy,RMSE,QWK,HDIV,Macro_F1,Micro_F1,Weighted_F1,TP_0,TN_0,FP_0,FN_0,TP_1,TN_1,FP_1,FN_1,TP_2,TN_2,FP_2,FN_2,TP_3,TN_3,FP_3,FN_3,TP_4,TN_4,FP_4,FN_4,TP_5,TN_5,FP_5,FN_5,timestamp,id
+0.5362318840579711,30.072376462244492,0.4505920783993467,0.007246376811594235,0.3244639912039582,0.5362318840579711,0.518137852459147,0,137,0,1,0,138,0,0,5,123,5,5,42,44,28,24,27,58,29,24,0,126,2,10,2025-05-25 21:44:30,jbcs2025_mbert_base-C1-encoder_classification-C1

runs/base_models/mbert/jbcs2025_mbert_base-C1-encoder_classification-C1/run_inference_experiment.log

@@ -1,5 +1,5 @@
-[2025-05-25 20:13:20,819][__main__][INFO] - Starting inference experiment
-[2025-05-25 20:13:20,820][__main__][INFO] - cache_dir: /tmp/
+[2025-05-25 21:44:30,752][__main__][INFO] - Starting inference experiment
+[2025-05-25 21:44:30,753][__main__][INFO] - cache_dir: /tmp/
 dataset:
   name: kamel-usp/aes_enem_dataset
   split: JBCS2025
@@ -35,9 +35,9 @@ experiments:
     gradient_accumulation_steps: 1
     gradient_checkpointing: false
 
-[2025-05-25 20:13:20,821][__main__][INFO] - Running inference with fine-tuned HF model
-[2025-05-25 20:13:24,874][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/config.json
-[2025-05-25 20:13:24,875][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-05-25 21:44:30,754][__main__][INFO] - Running inference with fine-tuned HF model
+[2025-05-25 21:44:35,108][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/config.json
+[2025-05-25 21:44:35,109][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -67,14 +67,14 @@ experiments:
   "vocab_size": 119547
 }
 
-[2025-05-25 20:13:25,061][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/vocab.txt
-[2025-05-25 20:13:25,061][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/tokenizer.json
-[2025-05-25 20:13:25,062][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at None
-[2025-05-25 20:13:25,062][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at None
-[2025-05-25 20:13:25,062][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/tokenizer_config.json
-[2025-05-25 20:13:25,062][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
-[2025-05-25 20:13:25,062][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/config.json
-[2025-05-25 20:13:25,063][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-05-25 21:44:35,297][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/vocab.txt
+[2025-05-25 21:44:35,297][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/tokenizer.json
+[2025-05-25 21:44:35,297][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at None
+[2025-05-25 21:44:35,297][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at None
+[2025-05-25 21:44:35,297][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/tokenizer_config.json
+[2025-05-25 21:44:35,297][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
+[2025-05-25 21:44:35,297][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/config.json
+[2025-05-25 21:44:35,298][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -104,10 +104,10 @@ experiments:
   "vocab_size": 119547
 }
 
-[2025-05-25 20:13:25,226][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
-[2025-05-25 20:13:25,261][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_mbert_base-C1
-[2025-05-25 20:13:25,531][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_mbert_base-C1/snapshots/065d457ead3102211e83566d77783a845d0f3351/config.json
-[2025-05-25 20:13:25,532][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-05-25 21:44:35,466][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
+[2025-05-25 21:44:35,502][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_mbert_base-C1
+[2025-05-25 21:44:35,699][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_mbert_base-C1/snapshots/065d457ead3102211e83566d77783a845d0f3351/config.json
+[2025-05-25 21:44:35,700][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForSequenceClassification"
   ],
@@ -155,21 +155,21 @@ experiments:
   "vocab_size": 119547
 }
 
-[2025-05-25 20:13:25,668][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_mbert_base-C1/snapshots/065d457ead3102211e83566d77783a845d0f3351/model.safetensors
-[2025-05-25 20:13:25,706][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
+[2025-05-25 21:44:35,839][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_mbert_base-C1/snapshots/065d457ead3102211e83566d77783a845d0f3351/model.safetensors
+[2025-05-25 21:44:35,881][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
 
-[2025-05-25 20:13:25,706][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_mbert_base-C1.
+[2025-05-25 21:44:35,881][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_mbert_base-C1.
 If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForSequenceClassification for predictions without further training.
-[2025-05-25 20:13:25,746][transformers.training_args][INFO] - PyTorch: setting up devices
-[2025-05-25 20:13:25,782][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
-[2025-05-25 20:13:26,011][transformers.trainer][INFO] - Using auto half precision backend
-[2025-05-25 20:13:26,011][__main__][INFO] - Running inference on test dataset
-[2025-05-25 20:13:26,012][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: reference, supporting_text, id, id_prompt, prompt, essay_year, essay_text, grades. If reference, supporting_text, id, id_prompt, prompt, essay_year, essay_text, grades are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
-[2025-05-25 20:13:26,016][transformers.trainer][INFO] - 
+[2025-05-25 21:44:35,922][transformers.training_args][INFO] - PyTorch: setting up devices
+[2025-05-25 21:44:35,958][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
+[2025-05-25 21:44:36,200][transformers.trainer][INFO] - Using auto half precision backend
+[2025-05-25 21:44:36,200][__main__][INFO] - Running inference on test dataset
+[2025-05-25 21:44:36,201][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: essay_year, id_prompt, grades, essay_text, supporting_text, reference, id, prompt. If essay_year, id_prompt, grades, essay_text, supporting_text, reference, id, prompt are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
+[2025-05-25 21:44:36,205][transformers.trainer][INFO] - 
 ***** Running Prediction *****
-[2025-05-25 20:13:26,016][transformers.trainer][INFO] -   Num examples = 138
-[2025-05-25 20:13:26,016][transformers.trainer][INFO] -   Batch size = 16
-[2025-05-25 20:13:26,530][transformers][INFO] - {'accuracy': 0.5362318840579711, 'RMSE': 30.072376462244492, 'QWK': 0.4505920783993467, 'HDIV': 0.007246376811594235, 'Macro_F1': 0.3244639912039582, 'Micro_F1': 0.5362318840579711, 'Weighted_F1': 0.518137852459147, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(5), 'TN_1': np.int64(123), 'FP_1': np.int64(5), 'FN_1': np.int64(5), 'TP_2': np.int64(42), 'TN_2': np.int64(44), 'FP_2': np.int64(28), 'FN_2': np.int64(24), 'TP_3': np.int64(27), 'TN_3': np.int64(58), 'FP_3': np.int64(29), 'FN_3': np.int64(24), 'TP_4': np.int64(0), 'TN_4': np.int64(126), 'FP_4': np.int64(2), 'FN_4': np.int64(10)}
-[2025-05-25 20:13:26,541][__main__][INFO] - Inference results saved to jbcs2025_mbert_base-C1-encoder_classification-C1_inference_results.jsonl
-[2025-05-25 20:13:26,556][__main__][INFO] - Inference results: {'accuracy': 0.5362318840579711, 'RMSE': 30.072376462244492, 'QWK': 0.4505920783993467, 'HDIV': 0.007246376811594235, 'Macro_F1': 0.3244639912039582, 'Micro_F1': 0.5362318840579711, 'Weighted_F1': 0.518137852459147, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(5), 'TN_1': np.int64(123), 'FP_1': np.int64(5), 'FN_1': np.int64(5), 'TP_2': np.int64(42), 'TN_2': np.int64(44), 'FP_2': np.int64(28), 'FN_2': np.int64(24), 'TP_3': np.int64(27), 'TN_3': np.int64(58), 'FP_3': np.int64(29), 'FN_3': np.int64(24), 'TP_4': np.int64(0), 'TN_4': np.int64(126), 'FP_4': np.int64(2), 'FN_4': np.int64(10)}
-[2025-05-25 20:13:26,556][__main__][INFO] - Inference experiment completed
+[2025-05-25 21:44:36,205][transformers.trainer][INFO] -   Num examples = 138
+[2025-05-25 21:44:36,205][transformers.trainer][INFO] -   Batch size = 16
+[2025-05-25 21:44:36,725][transformers][INFO] - {'accuracy': 0.5362318840579711, 'RMSE': 30.072376462244492, 'QWK': 0.4505920783993467, 'HDIV': 0.007246376811594235, 'Macro_F1': 0.3244639912039582, 'Micro_F1': 0.5362318840579711, 'Weighted_F1': 0.518137852459147, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(0), 'TN_1': np.int64(138), 'FP_1': np.int64(0), 'FN_1': np.int64(0), 'TP_2': np.int64(5), 'TN_2': np.int64(123), 'FP_2': np.int64(5), 'FN_2': np.int64(5), 'TP_3': np.int64(42), 'TN_3': np.int64(44), 'FP_3': np.int64(28), 'FN_3': np.int64(24), 'TP_4': np.int64(27), 'TN_4': np.int64(58), 'FP_4': np.int64(29), 'FN_4': np.int64(24), 'TP_5': np.int64(0), 'TN_5': np.int64(126), 'FP_5': np.int64(2), 'FN_5': np.int64(10)}
+[2025-05-25 21:44:36,735][__main__][INFO] - Inference results saved to jbcs2025_mbert_base-C1-encoder_classification-C1_inference_results.jsonl
+[2025-05-25 21:44:36,756][__main__][INFO] - Inference results: {'accuracy': 0.5362318840579711, 'RMSE': 30.072376462244492, 'QWK': 0.4505920783993467, 'HDIV': 0.007246376811594235, 'Macro_F1': 0.3244639912039582, 'Micro_F1': 0.5362318840579711, 'Weighted_F1': 0.518137852459147, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(0), 'TN_1': np.int64(138), 'FP_1': np.int64(0), 'FN_1': np.int64(0), 'TP_2': np.int64(5), 'TN_2': np.int64(123), 'FP_2': np.int64(5), 'FN_2': np.int64(5), 'TP_3': np.int64(42), 'TN_3': np.int64(44), 'FP_3': np.int64(28), 'FN_3': np.int64(24), 'TP_4': np.int64(27), 'TN_4': np.int64(58), 'FP_4': np.int64(29), 'FN_4': np.int64(24), 'TP_5': np.int64(0), 'TN_5': np.int64(126), 'FP_5': np.int64(2), 'FN_5': np.int64(10)}
+[2025-05-25 21:44:36,756][__main__][INFO] - Inference experiment completed