msperka/aleph_bert_gimmel-finetuned-ner

README.md

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+---
+base_model: imvladikon/alephbertgimmel-base-512
+tags:
+- generated_from_trainer
+datasets:
+- nemo_corpus
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: aleph_bert_gimmel-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: nemo_corpus
+      type: nemo_corpus
+      config: flat_token
+      split: validation
+      args: flat_token
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8048128342245989
+    - name: Recall
+      type: recall
+      value: 0.8058902275769746
+    - name: F1
+      type: f1
+      value: 0.8053511705685619
+    - name: Accuracy
+      type: accuracy
+      value: 0.9723507148864592
+---
+
+<!-- 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. -->
+
+# aleph_bert_gimmel-finetuned-ner
+
+This model is a fine-tuned version of [imvladikon/alephbertgimmel-base-512](https://huggingface.co/imvladikon/alephbertgimmel-base-512) on the nemo_corpus dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1231
+- Precision: 0.8048
+- Recall: 0.8059
+- F1: 0.8054
+- Accuracy: 0.9724
+
+## 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
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.375         | 1.0   | 618  | 0.1505          | 0.7767    | 0.7590 | 0.7678 | 0.9660   |
+| 0.1377        | 2.0   | 1236 | 0.1240          | 0.8188    | 0.8046 | 0.8116 | 0.9720   |
+| 0.0967        | 3.0   | 1854 | 0.1231          | 0.8048    | 0.8059 | 0.8054 | 0.9724   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.0.1+cpu
+- Datasets 2.15.0
+- Tokenizers 0.15.0