updating the repo with the fine-tuned model

README.md

@@ -0,0 +1,85 @@
+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: uwb_atcc
+  results: []
+---
+
+<!-- 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. -->
+
+# uwb_atcc
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0098
+- Precision: 0.9760
+- Recall: 0.9741
+- F1: 0.9750
+- Accuracy: 0.9965
+
+## 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: 5e-05
+- train_batch_size: 64
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 1000
+- training_steps: 10000
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 0.03  | 500   | 0.2282          | 0.6818    | 0.7001 | 0.6908 | 0.9246   |
+| 0.3487        | 0.06  | 1000  | 0.1214          | 0.8163    | 0.8024 | 0.8093 | 0.9631   |
+| 0.3487        | 0.1   | 1500  | 0.0933          | 0.8496    | 0.8544 | 0.8520 | 0.9722   |
+| 0.1124        | 0.13  | 2000  | 0.0693          | 0.8845    | 0.8739 | 0.8791 | 0.9786   |
+| 0.1124        | 0.16  | 2500  | 0.0540          | 0.8993    | 0.8911 | 0.8952 | 0.9817   |
+| 0.0667        | 0.19  | 3000  | 0.0474          | 0.9058    | 0.8929 | 0.8993 | 0.9857   |
+| 0.0667        | 0.23  | 3500  | 0.0418          | 0.9221    | 0.9245 | 0.9233 | 0.9865   |
+| 0.0492        | 0.26  | 4000  | 0.0294          | 0.9369    | 0.9415 | 0.9392 | 0.9903   |
+| 0.0492        | 0.29  | 4500  | 0.0263          | 0.9512    | 0.9446 | 0.9479 | 0.9911   |
+| 0.0372        | 0.32  | 5000  | 0.0223          | 0.9495    | 0.9497 | 0.9496 | 0.9915   |
+| 0.0372        | 0.35  | 5500  | 0.0212          | 0.9530    | 0.9514 | 0.9522 | 0.9923   |
+| 0.0308        | 0.39  | 6000  | 0.0177          | 0.9585    | 0.9560 | 0.9572 | 0.9933   |
+| 0.0308        | 0.42  | 6500  | 0.0169          | 0.9619    | 0.9613 | 0.9616 | 0.9936   |
+| 0.0261        | 0.45  | 7000  | 0.0140          | 0.9689    | 0.9662 | 0.9676 | 0.9951   |
+| 0.0261        | 0.48  | 7500  | 0.0130          | 0.9652    | 0.9629 | 0.9641 | 0.9945   |
+| 0.0214        | 0.51  | 8000  | 0.0127          | 0.9676    | 0.9635 | 0.9656 | 0.9953   |
+| 0.0214        | 0.55  | 8500  | 0.0109          | 0.9714    | 0.9708 | 0.9711 | 0.9959   |
+| 0.0177        | 0.58  | 9000  | 0.0103          | 0.9740    | 0.9727 | 0.9734 | 0.9961   |
+| 0.0177        | 0.61  | 9500  | 0.0101          | 0.9768    | 0.9744 | 0.9756 | 0.9963   |
+| 0.0159        | 0.64  | 10000 | 0.0098          | 0.9760    | 0.9741 | 0.9750 | 0.9965   |
+
+
+### Framework versions
+
+- Transformers 4.24.0
+- Pytorch 1.13.0+cu117
+- Datasets 2.7.0
+- Tokenizers 0.13.2

all_results.json

@@ -0,0 +1,7 @@
+{
+    "epoch": 0.64,
+    "train_loss": 0.07261505832672119,
+    "train_runtime": 1985.8061,
+    "train_samples_per_second": 322.287,
+    "train_steps_per_second": 5.036
+}

classification_report

@@ -0,0 +1,27 @@
+	************* Report B/I tags*************	
+              precision    recall  f1-score   support
+
+      B-atco       0.83      0.81      0.82      1356
+     B-pilot       0.79      0.89      0.83      1653
+      I-atco       0.95      0.90      0.93     13501
+     I-pilot       0.89      0.92      0.90     10216
+
+    accuracy                           0.91     26726
+   macro avg       0.86      0.88      0.87     26726
+weighted avg       0.91      0.91      0.91     26726
+
+ 	************ Report with merged classes ***********	
+              precision    recall  f1-score   support
+
+        atco       0.94      0.90      0.92     14857
+       pilot       0.88      0.93      0.90     11869
+
+    accuracy                           0.91     26726
+   macro avg       0.91      0.91      0.91     26726
+weighted avg       0.91      0.91      0.91     26726
+
+
+ JACCARD ERROR RATE (JER): [30.08233059 28.35603113 13.8444178  17.4533672 ]
+ JER - WEIGHTED          : 16.945343251112565 
+
+

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "experiments/results/augmented/bert-base-uncased/1234/uwb_atcc/final_checkpoint/",
+  "_name_or_path": "experiments/for_hf/bert-base-uncased/1234/uwb_atcc//",
   "architectures": [
     "BertForTokenClassification"
   ],
@@ -10,20 +10,20 @@
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
   "id2label": {
-    "0": "I-atco",
-    "1": "I-pilot",
+    "0": "O",
+    "1": "B-pilot",
     "2": "B-atco",
-    "3": "B-pilot",
-    "4": "O"
+    "3": "I-pilot",
+    "4": "I-atco"
   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
     "B-atco": 2,
-    "B-pilot": 3,
-    "I-atco": 0,
-    "I-pilot": 1,
-    "O": 4
+    "B-pilot": 1,
+    "I-atco": 4,
+    "I-pilot": 3,
+    "O": 0
   },
   "layer_norm_eps": 1e-12,
   "max_position_embeddings": 512,

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f20b48ff65ec066179d91cd95b2fa2ce74140945d6573d6e94fd735ed4b1991e
+oid sha256:3046900d4cc6ddf1e891841510afdfa4c0c486f6e2c9519fd11ec583496f5f67
 size 435651309

tokenizer_config.json

@@ -3,7 +3,7 @@
   "do_lower_case": true,
   "mask_token": "[MASK]",
   "model_max_length": 512,
-  "name_or_path": "experiments/results/augmented/bert-base-uncased/1234/uwb_atcc/final_checkpoint/",
+  "name_or_path": "experiments/for_hf/bert-base-uncased/1234/uwb_atcc//",
   "pad_token": "[PAD]",
   "sep_token": "[SEP]",
   "special_tokens_map_file": null,

train_results.json

@@ -0,0 +1,7 @@
+{
+    "epoch": 0.64,
+    "train_loss": 0.07261505832672119,
+    "train_runtime": 1985.8061,
+    "train_samples_per_second": 322.287,
+    "train_steps_per_second": 5.036
+}

trainer_state.json

@@ -0,0 +1,325 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 0.6432109088570143,
+  "global_step": 10000,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.03,
+      "eval_accuracy": 0.9246465960844621,
+      "eval_f1": 0.6907879797138747,
+      "eval_loss": 0.22821231186389923,
+      "eval_precision": 0.6817570596145226,
+      "eval_recall": 0.7000613685179503,
+      "eval_runtime": 6.5662,
+      "eval_samples_per_second": 761.48,
+      "eval_steps_per_second": 47.669,
+      "step": 500
+    },
+    {
+      "epoch": 0.06,
+      "learning_rate": 5e-05,
+      "loss": 0.3487,
+      "step": 1000
+    },
+    {
+      "epoch": 0.06,
+      "eval_accuracy": 0.9631217838765008,
+      "eval_f1": 0.809284332688588,
+      "eval_loss": 0.12138580530881882,
+      "eval_precision": 0.8162946776962697,
+      "eval_recall": 0.8023933722000613,
+      "eval_runtime": 6.1509,
+      "eval_samples_per_second": 812.889,
+      "eval_steps_per_second": 50.887,
+      "step": 1000
+    },
+    {
+      "epoch": 0.1,
+      "eval_accuracy": 0.972200863547643,
+      "eval_f1": 0.8519850072668859,
+      "eval_loss": 0.09331324696540833,
+      "eval_precision": 0.849580472921434,
+      "eval_recall": 0.8544031911629334,
+      "eval_runtime": 6.1413,
+      "eval_samples_per_second": 814.163,
+      "eval_steps_per_second": 50.967,
+      "step": 1500
+    },
+    {
+      "epoch": 0.13,
+      "learning_rate": 4.4444444444444447e-05,
+      "loss": 0.1124,
+      "step": 2000
+    },
+    {
+      "epoch": 0.13,
+      "eval_accuracy": 0.9786478973206364,
+      "eval_f1": 0.879148016669239,
+      "eval_loss": 0.06928630918264389,
+      "eval_precision": 0.884472049689441,
+      "eval_recall": 0.873887695612151,
+      "eval_runtime": 6.1606,
+      "eval_samples_per_second": 811.616,
+      "eval_steps_per_second": 50.807,
+      "step": 2000
+    },
+    {
+      "epoch": 0.16,
+      "eval_accuracy": 0.9816939729106288,
+      "eval_f1": 0.8951911220715166,
+      "eval_loss": 0.05397827923297882,
+      "eval_precision": 0.899349643852586,
+      "eval_recall": 0.8910708806382326,
+      "eval_runtime": 6.3033,
+      "eval_samples_per_second": 793.233,
+      "eval_steps_per_second": 49.656,
+      "step": 2500
+    },
+    {
+      "epoch": 0.19,
+      "learning_rate": 3.888888888888889e-05,
+      "loss": 0.0667,
+      "step": 3000
+    },
+    {
+      "epoch": 0.19,
+      "eval_accuracy": 0.9856568285325604,
+      "eval_f1": 0.8993278219887197,
+      "eval_loss": 0.0473916195333004,
+      "eval_precision": 0.9058365758754864,
+      "eval_recall": 0.8929119361767414,
+      "eval_runtime": 6.1626,
+      "eval_samples_per_second": 811.344,
+      "eval_steps_per_second": 50.79,
+      "step": 3000
+    },
+    {
+      "epoch": 0.23,
+      "eval_accuracy": 0.9864553143668303,
+      "eval_f1": 0.9233126484333103,
+      "eval_loss": 0.04179921746253967,
+      "eval_precision": 0.9221117061973986,
+      "eval_recall": 0.9245167229211415,
+      "eval_runtime": 6.1411,
+      "eval_samples_per_second": 814.192,
+      "eval_steps_per_second": 50.968,
+      "step": 3500
+    },
+    {
+      "epoch": 0.26,
+      "learning_rate": 3.3333333333333335e-05,
+      "loss": 0.0492,
+      "step": 4000
+    },
+    {
+      "epoch": 0.26,
+      "eval_accuracy": 0.9902850890163838,
+      "eval_f1": 0.9392408937863483,
+      "eval_loss": 0.02944410778582096,
+      "eval_precision": 0.9369465648854962,
+      "eval_recall": 0.9415464866523473,
+      "eval_runtime": 6.1192,
+      "eval_samples_per_second": 817.096,
+      "eval_steps_per_second": 51.15,
+      "step": 4000
+    },
+    {
+      "epoch": 0.29,
+      "eval_accuracy": 0.9910687880759449,
+      "eval_f1": 0.9478869986914018,
+      "eval_loss": 0.026342397555708885,
+      "eval_precision": 0.9511818322261703,
+      "eval_recall": 0.944614912549862,
+      "eval_runtime": 6.3093,
+      "eval_samples_per_second": 792.485,
+      "eval_steps_per_second": 49.61,
+      "step": 4500
+    },
+    {
+      "epoch": 0.32,
+      "learning_rate": 2.777777777777778e-05,
+      "loss": 0.0372,
+      "step": 5000
+    },
+    {
+      "epoch": 0.32,
+      "eval_accuracy": 0.9915419648666233,
+      "eval_f1": 0.9496049704686661,
+      "eval_loss": 0.022303204983472824,
+      "eval_precision": 0.9495321368308023,
+      "eval_recall": 0.949677815280761,
+      "eval_runtime": 6.0806,
+      "eval_samples_per_second": 822.284,
+      "eval_steps_per_second": 51.475,
+      "step": 5000
+    },
+    {
+      "epoch": 0.35,
+      "eval_accuracy": 0.9923256639261844,
+      "eval_f1": 0.9521689059500958,
+      "eval_loss": 0.02121582068502903,
+      "eval_precision": 0.9529737206085753,
+      "eval_recall": 0.951365449524394,
+      "eval_runtime": 6.1313,
+      "eval_samples_per_second": 815.488,
+      "eval_steps_per_second": 51.05,
+      "step": 5500
+    },
+    {
+      "epoch": 0.39,
+      "learning_rate": 2.2222222222222223e-05,
+      "loss": 0.0308,
+      "step": 6000
+    },
+    {
+      "epoch": 0.39,
+      "eval_accuracy": 0.993331164606376,
+      "eval_f1": 0.9572163760657499,
+      "eval_loss": 0.01765192300081253,
+      "eval_precision": 0.9584679280110752,
+      "eval_recall": 0.9559680883706658,
+      "eval_runtime": 6.2521,
+      "eval_samples_per_second": 799.734,
+      "eval_steps_per_second": 50.063,
+      "step": 6000
+    },
+    {
+      "epoch": 0.42,
+      "eval_accuracy": 0.9936269001005501,
+      "eval_f1": 0.9616329036218538,
+      "eval_loss": 0.016909204423427582,
+      "eval_precision": 0.9619281547436291,
+      "eval_recall": 0.9613378336913163,
+      "eval_runtime": 6.055,
+      "eval_samples_per_second": 825.759,
+      "eval_steps_per_second": 51.693,
+      "step": 6500
+    },
+    {
+      "epoch": 0.45,
+      "learning_rate": 1.6666666666666667e-05,
+      "loss": 0.0261,
+      "step": 7000
+    },
+    {
+      "epoch": 0.45,
+      "eval_accuracy": 0.9951055775714202,
+      "eval_f1": 0.9675833461361193,
+      "eval_loss": 0.013973040506243706,
+      "eval_precision": 0.9689230769230769,
+      "eval_recall": 0.9662473151273396,
+      "eval_runtime": 6.0884,
+      "eval_samples_per_second": 821.237,
+      "eval_steps_per_second": 51.409,
+      "step": 7000
+    },
+    {
+      "epoch": 0.48,
+      "eval_accuracy": 0.9945436801324895,
+      "eval_f1": 0.9640552995391705,
+      "eval_loss": 0.013004946522414684,
+      "eval_precision": 0.9652414641648723,
+      "eval_recall": 0.9628720466400736,
+      "eval_runtime": 6.2303,
+      "eval_samples_per_second": 802.527,
+      "eval_steps_per_second": 50.238,
+      "step": 7500
+    },
+    {
+      "epoch": 0.51,
+      "learning_rate": 1.1111111111111112e-05,
+      "loss": 0.0214,
+      "step": 8000
+    },
+    {
+      "epoch": 0.51,
+      "eval_accuracy": 0.9952534453185071,
+      "eval_f1": 0.965559655596556,
+      "eval_loss": 0.01268522348254919,
+      "eval_precision": 0.9676425269645609,
+      "eval_recall": 0.9634857318195765,
+      "eval_runtime": 6.0895,
+      "eval_samples_per_second": 821.082,
+      "eval_steps_per_second": 51.4,
+      "step": 8000
+    },
+    {
+      "epoch": 0.55,
+      "eval_accuracy": 0.9958744898562726,
+      "eval_f1": 0.971147943523634,
+      "eval_loss": 0.010873218066990376,
+      "eval_precision": 0.9714461160577218,
+      "eval_recall": 0.9708499539736115,
+      "eval_runtime": 6.1229,
+      "eval_samples_per_second": 816.611,
+      "eval_steps_per_second": 51.12,
+      "step": 8500
+    },
+    {
+      "epoch": 0.58,
+      "learning_rate": 5.555555555555556e-06,
+      "loss": 0.0177,
+      "step": 9000
+    },
+    {
+      "epoch": 0.58,
+      "eval_accuracy": 0.9961406518010292,
+      "eval_f1": 0.9733630152759652,
+      "eval_loss": 0.010330266319215298,
+      "eval_precision": 0.9740359502227685,
+      "eval_recall": 0.9726910095121203,
+      "eval_runtime": 6.0936,
+      "eval_samples_per_second": 820.536,
+      "eval_steps_per_second": 51.366,
+      "step": 9000
+    },
+    {
+      "epoch": 0.61,
+      "eval_accuracy": 0.9963328798722423,
+      "eval_f1": 0.9755760368663594,
+      "eval_loss": 0.010118774138391018,
+      "eval_precision": 0.9767763764995386,
+      "eval_recall": 0.9743786437557533,
+      "eval_runtime": 6.2563,
+      "eval_samples_per_second": 799.199,
+      "eval_steps_per_second": 50.03,
+      "step": 9500
+    },
+    {
+      "epoch": 0.64,
+      "learning_rate": 0.0,
+      "loss": 0.0159,
+      "step": 10000
+    },
+    {
+      "epoch": 0.64,
+      "eval_accuracy": 0.9965251079434554,
+      "eval_f1": 0.9750441526529986,
+      "eval_loss": 0.009783624671399593,
+      "eval_precision": 0.9760184473481937,
+      "eval_recall": 0.9740718011660019,
+      "eval_runtime": 6.0731,
+      "eval_samples_per_second": 823.307,
+      "eval_steps_per_second": 51.539,
+      "step": 10000
+    },
+    {
+      "epoch": 0.64,
+      "step": 10000,
+      "total_flos": 2.25374565312e+16,
+      "train_loss": 0.07261505832672119,
+      "train_runtime": 1985.8061,
+      "train_samples_per_second": 322.287,
+      "train_steps_per_second": 5.036
+    }
+  ],
+  "max_steps": 10000,
+  "num_train_epochs": 1,
+  "total_flos": 2.25374565312e+16,
+  "trial_name": null,
+  "trial_params": null
+}

training_args.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3cdeb0168688c5472b0cbaf00553a863c3e5b65633186e334bcaaa59634e487a
-size 3451
+oid sha256:2f1e174eb0134e9ea6d50af2aeb815175787e869220991a36ce7eeb1be2851c9
+size 3387