End of training

README.md

@@ -0,0 +1,76 @@
+---
+license: mit
+base_model: FacebookAI/xlm-roberta-large
+tags:
+- generated_from_trainer
+model-index:
+- name: roberta-large-ner-ghtk-cs-6-label-new-data-3090-6Sep-1
+  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. -->
+
+# roberta-large-ner-ghtk-cs-6-label-new-data-3090-6Sep-1
+
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1725
+- Tk: {'precision': 0.8651685393258427, 'recall': 0.6637931034482759, 'f1': 0.751219512195122, 'number': 116}
+- Gày: {'precision': 0.75, 'recall': 0.8823529411764706, 'f1': 0.8108108108108107, 'number': 34}
+- Gày trừu tượng: {'precision': 0.9111111111111111, 'recall': 0.9241803278688525, 'f1': 0.9175991861648015, 'number': 488}
+- Ã đơn: {'precision': 0.8958333333333334, 'recall': 0.8472906403940886, 'f1': 0.8708860759493672, 'number': 203}
+- Đt: {'precision': 0.9220917822838848, 'recall': 0.9840546697038725, 'f1': 0.9520661157024795, 'number': 878}
+- Đt trừu tượng: {'precision': 0.8170731707317073, 'recall': 0.8626609442060086, 'f1': 0.8392484342379958, 'number': 233}
+- Overall Precision: 0.8979
+- Overall Recall: 0.9196
+- Overall F1: 0.9086
+- Overall Accuracy: 0.9685
+
+## 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: 2.5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Tk                                                                                                       | Gày                                                                                                      | Gày trừu tượng                                                                                           | Ã đơn                                                                                                    | Đt                                                                                                       | Đt trừu tượng                                                                                            | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| No log        | 1.0   | 467  | 0.1594          | {'precision': 0.75, 'recall': 0.07758620689655173, 'f1': 0.14062500000000003, 'number': 116}             | {'precision': 0.30120481927710846, 'recall': 0.7352941176470589, 'f1': 0.4273504273504274, 'number': 34} | {'precision': 0.8500986193293886, 'recall': 0.8831967213114754, 'f1': 0.8663316582914573, 'number': 488} | {'precision': 0.777292576419214, 'recall': 0.8768472906403941, 'f1': 0.8240740740740742, 'number': 203}  | {'precision': 0.8541666666666666, 'recall': 0.9806378132118451, 'f1': 0.9130434782608696, 'number': 878} | {'precision': 0.755868544600939, 'recall': 0.6909871244635193, 'f1': 0.7219730941704036, 'number': 233}  | 0.8114            | 0.8530         | 0.8317     | 0.9466           |
+| 0.2588        | 2.0   | 934  | 0.1616          | {'precision': 0.578125, 'recall': 0.31896551724137934, 'f1': 0.41111111111111115, 'number': 116}         | {'precision': 0.5918367346938775, 'recall': 0.8529411764705882, 'f1': 0.6987951807228915, 'number': 34}  | {'precision': 0.8096947935368043, 'recall': 0.9241803278688525, 'f1': 0.8631578947368421, 'number': 488} | {'precision': 0.9083969465648855, 'recall': 0.5862068965517241, 'f1': 0.7125748502994012, 'number': 203} | {'precision': 0.8858307849133538, 'recall': 0.989749430523918, 'f1': 0.9349112426035503, 'number': 878}  | {'precision': 0.5903614457831325, 'recall': 0.8412017167381974, 'f1': 0.6938053097345134, 'number': 233} | 0.8046            | 0.8714         | 0.8367     | 0.9465           |
+| 0.1107        | 3.0   | 1401 | 0.1661          | {'precision': 0.8070175438596491, 'recall': 0.7931034482758621, 'f1': 0.8, 'number': 116}                | {'precision': 0.5370370370370371, 'recall': 0.8529411764705882, 'f1': 0.6590909090909091, 'number': 34}  | {'precision': 0.8352272727272727, 'recall': 0.9036885245901639, 'f1': 0.8681102362204726, 'number': 488} | {'precision': 0.9352941176470588, 'recall': 0.7832512315270936, 'f1': 0.8525469168900804, 'number': 203} | {'precision': 0.9374325782092773, 'recall': 0.989749430523918, 'f1': 0.9628808864265929, 'number': 878}  | {'precision': 0.6057692307692307, 'recall': 0.8111587982832618, 'f1': 0.6935779816513762, 'number': 233} | 0.8451            | 0.9114         | 0.8770     | 0.9562           |
+| 0.0835        | 4.0   | 1868 | 0.1308          | {'precision': 0.8243243243243243, 'recall': 0.5258620689655172, 'f1': 0.6421052631578947, 'number': 116} | {'precision': 0.7741935483870968, 'recall': 0.7058823529411765, 'f1': 0.7384615384615385, 'number': 34}  | {'precision': 0.8868686868686869, 'recall': 0.8995901639344263, 'f1': 0.8931841302136317, 'number': 488} | {'precision': 0.8695652173913043, 'recall': 0.7881773399014779, 'f1': 0.82687338501292, 'number': 203}   | {'precision': 0.9097586568730325, 'recall': 0.9874715261958997, 'f1': 0.947023484434735, 'number': 878}  | {'precision': 0.684931506849315, 'recall': 0.8583690987124464, 'f1': 0.7619047619047619, 'number': 233}  | 0.8630            | 0.8970         | 0.8797     | 0.9594           |
+| 0.0594        | 5.0   | 2335 | 0.1368          | {'precision': 0.6888888888888889, 'recall': 0.8017241379310345, 'f1': 0.7410358565737052, 'number': 116} | {'precision': 0.75, 'recall': 0.7058823529411765, 'f1': 0.7272727272727272, 'number': 34}                | {'precision': 0.9032258064516129, 'recall': 0.9180327868852459, 'f1': 0.9105691056910569, 'number': 488} | {'precision': 0.9090909090909091, 'recall': 0.7881773399014779, 'f1': 0.8443271767810027, 'number': 203} | {'precision': 0.935659760087241, 'recall': 0.9772209567198178, 'f1': 0.9559888579387186, 'number': 878}  | {'precision': 0.7751937984496124, 'recall': 0.8583690987124464, 'f1': 0.814663951120163, 'number': 233}  | 0.8853            | 0.9134         | 0.8991     | 0.9647           |
+| 0.044         | 6.0   | 2802 | 0.1338          | {'precision': 0.9333333333333333, 'recall': 0.603448275862069, 'f1': 0.7329842931937173, 'number': 116}  | {'precision': 0.6744186046511628, 'recall': 0.8529411764705882, 'f1': 0.7532467532467532, 'number': 34}  | {'precision': 0.9276595744680851, 'recall': 0.8934426229508197, 'f1': 0.9102296450939458, 'number': 488} | {'precision': 0.875, 'recall': 0.8275862068965517, 'f1': 0.8506329113924052, 'number': 203}              | {'precision': 0.924468085106383, 'recall': 0.989749430523918, 'f1': 0.955995599559956, 'number': 878}    | {'precision': 0.7807692307692308, 'recall': 0.871244635193133, 'f1': 0.8235294117647058, 'number': 233}  | 0.8965            | 0.9093         | 0.9028     | 0.9658           |
+| 0.0264        | 7.0   | 3269 | 0.1551          | {'precision': 0.7857142857142857, 'recall': 0.6637931034482759, 'f1': 0.719626168224299, 'number': 116}  | {'precision': 0.6041666666666666, 'recall': 0.8529411764705882, 'f1': 0.7073170731707317, 'number': 34}  | {'precision': 0.888015717092338, 'recall': 0.9262295081967213, 'f1': 0.9067201604814443, 'number': 488}  | {'precision': 0.848780487804878, 'recall': 0.8571428571428571, 'f1': 0.8529411764705881, 'number': 203}  | {'precision': 0.924468085106383, 'recall': 0.989749430523918, 'f1': 0.955995599559956, 'number': 878}    | {'precision': 0.8253275109170306, 'recall': 0.8111587982832618, 'f1': 0.8181818181818181, 'number': 233} | 0.8822            | 0.9170         | 0.8993     | 0.9641           |
+| 0.02          | 8.0   | 3736 | 0.1654          | {'precision': 0.9240506329113924, 'recall': 0.6293103448275862, 'f1': 0.7487179487179487, 'number': 116} | {'precision': 0.7142857142857143, 'recall': 0.8823529411764706, 'f1': 0.7894736842105262, 'number': 34}  | {'precision': 0.9001996007984032, 'recall': 0.9241803278688525, 'f1': 0.9120323559150658, 'number': 488} | {'precision': 0.9, 'recall': 0.8423645320197044, 'f1': 0.8702290076335878, 'number': 203}                | {'precision': 0.9234913793103449, 'recall': 0.9760820045558086, 'f1': 0.9490586932447398, 'number': 878} | {'precision': 0.8031496062992126, 'recall': 0.8755364806866953, 'f1': 0.837782340862423, 'number': 233}  | 0.8957            | 0.9150         | 0.9052     | 0.9676           |
+| 0.0107        | 9.0   | 4203 | 0.1804          | {'precision': 0.8390804597701149, 'recall': 0.6293103448275862, 'f1': 0.7192118226600984, 'number': 116} | {'precision': 0.725, 'recall': 0.8529411764705882, 'f1': 0.7837837837837837, 'number': 34}               | {'precision': 0.907258064516129, 'recall': 0.9221311475409836, 'f1': 0.9146341463414632, 'number': 488}  | {'precision': 0.8693467336683417, 'recall': 0.8522167487684729, 'f1': 0.8606965174129352, 'number': 203} | {'precision': 0.917558886509636, 'recall': 0.9760820045558086, 'f1': 0.945916114790287, 'number': 878}   | {'precision': 0.8355555555555556, 'recall': 0.8068669527896996, 'f1': 0.8209606986899564, 'number': 233} | 0.8935            | 0.9068         | 0.9001     | 0.9661           |
+| 0.009         | 10.0  | 4670 | 0.1725          | {'precision': 0.8651685393258427, 'recall': 0.6637931034482759, 'f1': 0.751219512195122, 'number': 116}  | {'precision': 0.75, 'recall': 0.8823529411764706, 'f1': 0.8108108108108107, 'number': 34}                | {'precision': 0.9111111111111111, 'recall': 0.9241803278688525, 'f1': 0.9175991861648015, 'number': 488} | {'precision': 0.8958333333333334, 'recall': 0.8472906403940886, 'f1': 0.8708860759493672, 'number': 203} | {'precision': 0.9220917822838848, 'recall': 0.9840546697038725, 'f1': 0.9520661157024795, 'number': 878} | {'precision': 0.8170731707317073, 'recall': 0.8626609442060086, 'f1': 0.8392484342379958, 'number': 233} | 0.8979            | 0.9196         | 0.9086     | 0.9685           |
+
+
+### Framework versions
+
+- Transformers 4.44.0
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

model.safetensors

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