upload model

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: reco-ner
+  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. -->
+
+# reco-ner
+
+This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0668
+- Precision: 0.8125
+- Recall: 0.8790
+- F1: 0.8444
+- Accuracy: 0.9819
+
+## 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: 16
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.4516        | 1.0   | 626  | 0.4047          | 0.4332    | 0.4564 | 0.4445 | 0.8980   |
+| 0.3677        | 2.0   | 1252 | 0.2774          | 0.4918    | 0.5731 | 0.5293 | 0.9193   |
+| 0.2892        | 3.0   | 1878 | 0.2133          | 0.6139    | 0.6581 | 0.6353 | 0.9384   |
+| 0.2736        | 4.0   | 2504 | 0.1772          | 0.6248    | 0.6854 | 0.6537 | 0.9488   |
+| 0.221         | 5.0   | 3130 | 0.1503          | 0.6295    | 0.7328 | 0.6772 | 0.9560   |
+| 0.1569        | 6.0   | 3756 | 0.1283          | 0.6821    | 0.8108 | 0.7409 | 0.9623   |
+| 0.1534        | 7.0   | 4382 | 0.0995          | 0.7412    | 0.8119 | 0.7749 | 0.9708   |
+| 0.089         | 8.0   | 5008 | 0.0846          | 0.7695    | 0.8353 | 0.8010 | 0.9760   |
+| 0.0923        | 9.0   | 5634 | 0.0743          | 0.7881    | 0.8740 | 0.8289 | 0.9789   |
+| 0.0711        | 10.0  | 6260 | 0.0668          | 0.8125    | 0.8790 | 0.8444 | 0.9819   |
+
+
+### Framework versions
+
+- Transformers 4.22.2
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1

added_tokens.json

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+}

all_results.json

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+    "eval_precision": 0.8125,
+    "eval_recall": 0.8789531079607416,
+    "eval_runtime": 68.0971,
+    "eval_samples": 10007,
+    "eval_samples_per_second": 146.952,
+    "eval_steps_per_second": 36.742,
+    "train_loss": 0.217802461039144,
+    "train_runtime": 2284.8138,
+    "train_samples": 10007,
+    "train_samples_per_second": 43.798,
+    "train_steps_per_second": 2.74
+}

config.json

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+  "architectures": [
+    "DebertaV2ForTokenClassification"
+  ],
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+  "finetuning_task": "ner",
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eval_results.json

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special_tokens_map.json

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training_args.bin

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