Training complete

README.md

@@ -0,0 +1,91 @@
+---
+base_model: mHossain/bengali_pos_v1
+tags:
+- generated_from_trainer
+datasets:
+- pos_tag_100k
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bengali_pos_v1_200000
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: pos_tag_100k
+      type: pos_tag_100k
+      config: conll2003
+      split: validation
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7727710210867093
+    - name: Recall
+      type: recall
+      value: 0.7751739702839947
+    - name: F1
+      type: f1
+      value: 0.7739706305787504
+    - name: Accuracy
+      type: accuracy
+      value: 0.8325531771739444
+---
+
+<!-- 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. -->
+
+# bengali_pos_v1_200000
+
+This model is a fine-tuned version of [mHossain/bengali_pos_v1](https://huggingface.co/mHossain/bengali_pos_v1) on the pos_tag_100k dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5802
+- Precision: 0.7728
+- Recall: 0.7752
+- F1: 0.7740
+- Accuracy: 0.8326
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.6183        | 1.0   | 22500 | 0.6032          | 0.7546    | 0.7570 | 0.7558 | 0.8193   |
+| 0.5138        | 2.0   | 45000 | 0.5763          | 0.7691    | 0.7694 | 0.7692 | 0.8292   |
+| 0.4448        | 3.0   | 67500 | 0.5802          | 0.7728    | 0.7752 | 0.7740 | 0.8326   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0