Model save

README.md

@@ -0,0 +1,89 @@
+---
+library_name: transformers
+base_model: FacebookAI/xlm-roberta-large-finetuned-conll03-english
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: xlm-roberta-large-clinical-ner-breast-cancer-sp
+  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. -->
+
+# xlm-roberta-large-clinical-ner-breast-cancer-sp
+
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-english) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3023
+- Precision: 0.8965
+- Recall: 0.8959
+- F1: 0.8962
+- Accuracy: 0.9474
+
+## 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: 1e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 64
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine_with_restarts
+- lr_scheduler_warmup_ratio: 0.2
+- num_epochs: 20
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.2687        | 1.0     | 213  | 1.3859          | 0.4556    | 0.3389 | 0.3887 | 0.6763   |
+| 0.4857        | 2.0     | 426  | 0.5022          | 0.7673    | 0.7919 | 0.7794 | 0.8970   |
+| 0.2519        | 3.0     | 639  | 0.3412          | 0.8407    | 0.8452 | 0.8430 | 0.9259   |
+| 0.1671        | 4.0     | 852  | 0.3058          | 0.8711    | 0.8659 | 0.8685 | 0.9355   |
+| 0.1423        | 5.0     | 1065 | 0.2983          | 0.8585    | 0.8659 | 0.8622 | 0.9340   |
+| 0.0973        | 6.0     | 1278 | 0.2795          | 0.8773    | 0.8732 | 0.8753 | 0.9397   |
+| 0.0655        | 7.0     | 1491 | 0.2775          | 0.8755    | 0.8726 | 0.8740 | 0.9393   |
+| 0.0734        | 8.0     | 1704 | 0.2755          | 0.8799    | 0.8846 | 0.8822 | 0.9422   |
+| 0.0575        | 9.0     | 1917 | 0.2900          | 0.8828    | 0.8793 | 0.8810 | 0.9409   |
+| 0.0522        | 10.0    | 2130 | 0.2852          | 0.8864    | 0.8846 | 0.8855 | 0.9417   |
+| 0.0559        | 11.0    | 2343 | 0.2735          | 0.8863    | 0.8893 | 0.8878 | 0.9441   |
+| 0.0401        | 12.0    | 2556 | 0.2845          | 0.8833    | 0.8939 | 0.8886 | 0.9434   |
+| 0.0326        | 13.0    | 2769 | 0.2845          | 0.8951    | 0.8933 | 0.8942 | 0.9462   |
+| 0.0513        | 14.0    | 2982 | 0.2864          | 0.8886    | 0.8886 | 0.8886 | 0.9453   |
+| 0.0223        | 15.0    | 3195 | 0.2920          | 0.8923    | 0.8899 | 0.8911 | 0.9455   |
+| 0.0332        | 16.0    | 3408 | 0.2956          | 0.8906    | 0.8906 | 0.8906 | 0.9470   |
+| 0.0262        | 17.0    | 3621 | 0.2987          | 0.8953    | 0.8959 | 0.8956 | 0.9469   |
+| 0.018         | 18.0    | 3834 | 0.2999          | 0.8965    | 0.8959 | 0.8962 | 0.9474   |
+| 0.02          | 19.0    | 4047 | 0.3023          | 0.8965    | 0.8959 | 0.8962 | 0.9472   |
+| 0.0222        | 19.9088 | 4240 | 0.3023          | 0.8965    | 0.8959 | 0.8962 | 0.9474   |
+
+
+### Framework versions
+
+- Transformers 4.48.2
+- Pytorch 2.5.1+cu124
+- Datasets 3.2.0
+- Tokenizers 0.21.0

config.json

@@ -6,6 +6,7 @@
   ],
   "attention_probs_dropout_prob": 0.1,
   "bos_token_id": 0,
+  "cancer_type": "mama",
   "classifier_dropout": null,
   "eos_token_id": 2,
   "hidden_act": "gelu",

model.safetensors

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