Training complete

README.md

@@ -1,74 +1,75 @@
----
-license: mit
-base_model: FacebookAI/roberta-base
-tags:
-- generated_from_trainer
-metrics:
-- precision
-- recall
-- f1
-- accuracy
-model-index:
-- name: RoBERTa-full-finetuned-ner-pablo
-  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. -->
-
-# RoBERTa-full-finetuned-ner-pablo
-
-This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1115
-- Precision: 0.8216
-- Recall: 0.8049
-- F1: 0.8131
-- Accuracy: 0.9728
-
-## 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: 0.0002
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 64
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.05
-- num_epochs: 5
-- mixed_precision_training: Native AMP
-
-### Training results
-
-| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.2748        | 0.9996 | 652  | 0.1191          | 0.7950    | 0.7296 | 0.7609 | 0.9687   |
-| 0.1112        | 1.9992 | 1304 | 0.1107          | 0.7677    | 0.7730 | 0.7703 | 0.9676   |
-| 0.0872        | 2.9989 | 1956 | 0.0980          | 0.8074    | 0.7827 | 0.7949 | 0.9716   |
-| 0.0564        | 4.0    | 2609 | 0.1038          | 0.8143    | 0.8048 | 0.8096 | 0.9727   |
-| 0.0446        | 4.9981 | 3260 | 0.1115          | 0.8216    | 0.8049 | 0.8131 | 0.9728   |
-
-
-### Framework versions
-
-- Transformers 4.44.0
-- Pytorch 2.4.0+cu124
-- Datasets 2.21.0
-- Tokenizers 0.19.1
+---
+library_name: transformers
+license: mit
+base_model: FacebookAI/roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: RoBERTa-full-finetuned-ner-pablo
+  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. -->
+
+# RoBERTa-full-finetuned-ner-pablo
+
+This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0908
+- Precision: 0.815
+- Recall: 0.8050
+- F1: 0.8100
+- Accuracy: 0.9767
+
+## 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: 0.0002
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.05
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 0.9970 | 252  | 0.1094          | 0.6862    | 0.7348 | 0.7097 | 0.9688   |
+| 0.1923        | 1.9980 | 505  | 0.0836          | 0.8005    | 0.7868 | 0.7936 | 0.9762   |
+| 0.1923        | 2.9990 | 758  | 0.0816          | 0.8109    | 0.7855 | 0.7980 | 0.9755   |
+| 0.0504        | 4.0    | 1011 | 0.0839          | 0.8073    | 0.8028 | 0.8051 | 0.9763   |
+| 0.0504        | 4.9852 | 1260 | 0.0908          | 0.815     | 0.8050 | 0.8100 | 0.9767   |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

runs/Aug23_14-46-34_ee1898c059d7/events.out.tfevents.1724424395.ee1898c059d7.1664.10

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