update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- token-classification
+- generated_from_trainer
+model-index:
+- name: bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd
+  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. -->
+
+# bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd
+
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1312
+- Erson: {'precision': 0.8860048426150121, 'recall': 0.9401849948612538, 'f1': 0.912291199202194, 'number': 29190}
+- Ocation: {'precision': 0.8686381704207632, 'recall': 0.8152889539136796, 'f1': 0.841118472477534, 'number': 95690}
+- Rganization: {'precision': 0.7919078915181266, 'recall': 0.7449641777764141, 'f1': 0.7677190874452579, 'number': 65183}
+- Roduct: {'precision': 0.7065968977761166, 'recall': 0.8295304958315051, 'f1': 0.7631446160056513, 'number': 9116}
+- Rt: {'precision': 0.8407258064516129, 'recall': 0.8614333386302241, 'f1': 0.8509536143159878, 'number': 6293}
+- Ther: {'precision': 0.7303024586555996, 'recall': 0.8314124132006586, 'f1': 0.7775843599357258, 'number': 13969}
+- Uilding: {'precision': 0.5162234691388143, 'recall': 0.3648904983617865, 'f1': 0.4275611234592847, 'number': 5799}
+- Vent: {'precision': 0.605920892987139, 'recall': 0.35144264602392683, 'f1': 0.44486014608943525, 'number': 7105}
+- Overall Precision: 0.8203
+- Overall Recall: 0.7886
+- Overall F1: 0.8041
+- Overall Accuracy: 0.9498
+
+## 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: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Erson                                                                                                      | Ocation                                                                                                    | Rganization                                                                                                | Roduct                                                                                                    | Rt                                                                                                        | Ther                                                                                                       | Uilding                                                                                                   | Vent                                                                                                       | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.1796        | 1.0   | 11293 | 0.1427          | {'precision': 0.8740795762821341, 'recall': 0.9272010962658445, 'f1': 0.8998570336137248, 'number': 29190} | {'precision': 0.8576076215009827, 'recall': 0.8071585327620441, 'f1': 0.8316186723086282, 'number': 95690} | {'precision': 0.7699032109387003, 'recall': 0.7688047497046776, 'f1': 0.7693535882339395, 'number': 65183} | {'precision': 0.6710836277974087, 'recall': 0.75, 'f1': 0.7083506009117282, 'number': 9116}               | {'precision': 0.834716121685375, 'recall': 0.8153503893214683, 'f1': 0.8249196141479099, 'number': 6293}  | {'precision': 0.6742843680056544, 'recall': 0.8195289569761615, 'f1': 0.7398455423789058, 'number': 13969} | {'precision': 0.4812014282713716, 'recall': 0.3950681151922745, 'f1': 0.4339015151515152, 'number': 5799} | {'precision': 0.5997923695821438, 'recall': 0.32526389866291344, 'f1': 0.4217922978645739, 'number': 7105} | 0.8000            | 0.7852         | 0.7925     | 0.9483           |
+| 0.1542        | 2.0   | 22586 | 0.1312          | {'precision': 0.8860048426150121, 'recall': 0.9401849948612538, 'f1': 0.912291199202194, 'number': 29190}  | {'precision': 0.8686381704207632, 'recall': 0.8152889539136796, 'f1': 0.841118472477534, 'number': 95690}  | {'precision': 0.7919078915181266, 'recall': 0.7449641777764141, 'f1': 0.7677190874452579, 'number': 65183} | {'precision': 0.7065968977761166, 'recall': 0.8295304958315051, 'f1': 0.7631446160056513, 'number': 9116} | {'precision': 0.8407258064516129, 'recall': 0.8614333386302241, 'f1': 0.8509536143159878, 'number': 6293} | {'precision': 0.7303024586555996, 'recall': 0.8314124132006586, 'f1': 0.7775843599357258, 'number': 13969} | {'precision': 0.5162234691388143, 'recall': 0.3648904983617865, 'f1': 0.4275611234592847, 'number': 5799} | {'precision': 0.605920892987139, 'recall': 0.35144264602392683, 'f1': 0.44486014608943525, 'number': 7105} | 0.8203            | 0.7886         | 0.8041     | 0.9498           |
+
+
+### Framework versions
+
+- Transformers 4.30.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.13.1
+- Tokenizers 0.13.3