Update README.md

README.md

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----
-license: apache-2.0
-base_model: distilbert-base-uncased
-tags:
-- generated_from_trainer
-datasets:
-- conll2002
-metrics:
-- precision
-- recall
-- f1
-- accuracy
-model-index:
-- name: distilbert-base-uncased-finetuned-ner
-  results:
-  - task:
-      name: Token Classification
-      type: token-classification
-    dataset:
-      name: conll2002
-      type: conll2002
-      config: es
-      split: validation
-      args: es
-    metrics:
-    - name: Precision
-      type: precision
-      value: 0.7348668280871671
-    - name: Recall
-      type: recall
-      value: 0.7311491206938088
-    - name: F1
-      type: f1
-      value: 0.733003260475788
-    - name: Accuracy
-      type: accuracy
-      value: 0.94996285742796
----
-
-<!-- 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. -->
-
-# distilbert-base-uncased-finetuned-ner
-
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2002 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.2347
-- Precision: 0.7349
-- Recall: 0.7311
-- F1: 0.7330
-- Accuracy: 0.9500
-
-## 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: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 10
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.3477        | 1.0   | 521  | 0.2581          | 0.6392    | 0.5888 | 0.6130 | 0.9270   |
-| 0.1883        | 2.0   | 1042 | 0.2224          | 0.6617    | 0.6644 | 0.6631 | 0.9370   |
-| 0.1339        | 3.0   | 1563 | 0.2079          | 0.7044    | 0.7021 | 0.7033 | 0.9431   |
-| 0.1039        | 4.0   | 2084 | 0.2040          | 0.7017    | 0.7221 | 0.7118 | 0.9446   |
-| 0.0835        | 5.0   | 2605 | 0.2126          | 0.7306    | 0.7166 | 0.7235 | 0.9486   |
-| 0.0647        | 6.0   | 3126 | 0.2221          | 0.7220    | 0.7198 | 0.7209 | 0.9478   |
-| 0.0536        | 7.0   | 3647 | 0.2258          | 0.7198    | 0.7244 | 0.7221 | 0.9480   |
-| 0.0443        | 8.0   | 4168 | 0.2319          | 0.7047    | 0.7334 | 0.7188 | 0.9469   |
-| 0.0375        | 9.0   | 4689 | 0.2350          | 0.7182    | 0.7315 | 0.7248 | 0.9482   |
-| 0.0349        | 10.0  | 5210 | 0.2347          | 0.7349    | 0.7311 | 0.7330 | 0.9500   |
-
-
-### Framework versions
-
-- Transformers 4.43.3
-- Pytorch 2.4.0
-- Datasets 2.20.0
-- Tokenizers 0.19.1
+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- conll2002
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: distilbert-base-uncased-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2002
+      type: conll2002
+      config: es
+      split: validation
+      args: es
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7348668280871671
+    - name: Recall
+      type: recall
+      value: 0.7311491206938088
+    - name: F1
+      type: f1
+      value: 0.733003260475788
+    - name: Accuracy
+      type: accuracy
+      value: 0.94996285742796
+pipeline_tag: token-classification
+---
+
+<!-- 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. -->
+
+# distilbert-base-uncased-finetuned-ner
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2002 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2347
+- Precision: 0.7349
+- Recall: 0.7311
+- F1: 0.7330
+- Accuracy: 0.9500
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.3477        | 1.0   | 521  | 0.2581          | 0.6392    | 0.5888 | 0.6130 | 0.9270   |
+| 0.1883        | 2.0   | 1042 | 0.2224          | 0.6617    | 0.6644 | 0.6631 | 0.9370   |
+| 0.1339        | 3.0   | 1563 | 0.2079          | 0.7044    | 0.7021 | 0.7033 | 0.9431   |
+| 0.1039        | 4.0   | 2084 | 0.2040          | 0.7017    | 0.7221 | 0.7118 | 0.9446   |
+| 0.0835        | 5.0   | 2605 | 0.2126          | 0.7306    | 0.7166 | 0.7235 | 0.9486   |
+| 0.0647        | 6.0   | 3126 | 0.2221          | 0.7220    | 0.7198 | 0.7209 | 0.9478   |
+| 0.0536        | 7.0   | 3647 | 0.2258          | 0.7198    | 0.7244 | 0.7221 | 0.9480   |
+| 0.0443        | 8.0   | 4168 | 0.2319          | 0.7047    | 0.7334 | 0.7188 | 0.9469   |
+| 0.0375        | 9.0   | 4689 | 0.2350          | 0.7182    | 0.7315 | 0.7248 | 0.9482   |
+| 0.0349        | 10.0  | 5210 | 0.2347          | 0.7349    | 0.7311 | 0.7330 | 0.9500   |
+
+
+### Framework versions
+
+- Transformers 4.43.3
+- Pytorch 2.4.0
+- Datasets 2.20.0
+- Tokenizers 0.19.1