End of training

README.md

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+---
+license: apache-2.0
+base_model: google/mt5-large
+tags:
+- generated_from_trainer
+model-index:
+- name: ner_cs
+  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. -->
+
+# ner_cs
+
+This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5017
+- Loc: {'precision': 0.8522895125553914, 'recall': 0.9058084772370487, 'f1': 0.878234398782344, 'number': 637}
+- Org: {'precision': 0.8361702127659575, 'recall': 0.8488120950323974, 'f1': 0.8424437299035369, 'number': 463}
+- Per: {'precision': 0.9230769230769231, 'recall': 0.9737470167064439, 'f1': 0.9477351916376306, 'number': 419}
+- Overall Precision: 0.8672
+- Overall Recall: 0.9072
+- Overall F1: 0.8867
+- Overall Accuracy: 0.9365
+
+## 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: 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: 20
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Loc                                                                                                      | Org                                                                                                      | Per                                                                                                      | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.2192        | 5.71  | 5000  | 0.2824          | {'precision': 0.8384728340675477, 'recall': 0.8963893249607535, 'f1': 0.8664643399089529, 'number': 637} | {'precision': 0.808641975308642, 'recall': 0.8488120950323974, 'f1': 0.8282402528977871, 'number': 463}  | {'precision': 0.9325581395348838, 'recall': 0.9570405727923628, 'f1': 0.944640753828033, 'number': 419}  | 0.8547            | 0.8986         | 0.8761     | 0.9363           |
+| 0.0244        | 11.43 | 10000 | 0.4134          | {'precision': 0.8622754491017964, 'recall': 0.9042386185243328, 'f1': 0.8827586206896552, 'number': 637} | {'precision': 0.841991341991342, 'recall': 0.8401727861771058, 'f1': 0.8410810810810811, 'number': 463}  | {'precision': 0.920814479638009, 'recall': 0.9713603818615751, 'f1': 0.9454123112659697, 'number': 419}  | 0.8728            | 0.9032         | 0.8877     | 0.9370           |
+| 0.0066        | 17.14 | 15000 | 0.5017          | {'precision': 0.8522895125553914, 'recall': 0.9058084772370487, 'f1': 0.878234398782344, 'number': 637}  | {'precision': 0.8361702127659575, 'recall': 0.8488120950323974, 'f1': 0.8424437299035369, 'number': 463} | {'precision': 0.9230769230769231, 'recall': 0.9737470167064439, 'f1': 0.9477351916376306, 'number': 419} | 0.8672            | 0.9072         | 0.8867     | 0.9365           |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 1.11.0a0+17540c5
+- Datasets 2.20.0
+- Tokenizers 0.15.2