End of training

.gitattributes

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 *.zst filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,72 @@
+---
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: xlm-roberta-base-IDMGSP-danish
+  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-base-IDMGSP-danish
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0276
+- Accuracy: {'accuracy': 0.8530452362901187}
+- F1: {'f1': 0.8630636995172495}
+
+## 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: 0.0001
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | F1                         |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:|
+| 0.3631        | 1.0   | 496  | 0.9902          | {'accuracy': 0.5959059893858984} | {'f1': 0.7104310032596887} |
+| 0.3208        | 2.0   | 992  | 1.0736          | {'accuracy': 0.7261814505938843} | {'f1': 0.780245411215901}  |
+| 0.2191        | 3.0   | 1488 | 0.3496          | {'accuracy': 0.8664392216325499} | {'f1': 0.8717077315208156} |
+| 0.1548        | 4.0   | 1984 | 0.5604          | {'accuracy': 0.8155168056608542} | {'f1': 0.8378858538751943} |
+| 0.1127        | 5.0   | 2480 | 0.4164          | {'accuracy': 0.8641647712913824} | {'f1': 0.871056735036584}  |
+| 0.1372        | 6.0   | 2976 | 0.5515          | {'accuracy': 0.8822340156684357} | {'f1': 0.8833833833833834} |
+| 0.0279        | 7.0   | 3472 | 0.7203          | {'accuracy': 0.8458428102097548} | {'f1': 0.8573766658873042} |
+| 0.0456        | 8.0   | 3968 | 0.8584          | {'accuracy': 0.8498862774829417} | {'f1': 0.8604651162790697} |
+| 0.0095        | 9.0   | 4464 | 0.9214          | {'accuracy': 0.8512762193580996} | {'f1': 0.861415283174379}  |
+| 0.0076        | 10.0  | 4960 | 1.0276          | {'accuracy': 0.8530452362901187} | {'f1': 0.8630636995172495} |
+
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch 2.0.1
+- Datasets 2.14.6
+- Tokenizers 0.14.1

adapter_config.json

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best_model/config.json

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