trainer: training complete at 2024-01-28 14:46:00.578662.

README.md

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+---
+license: apache-2.0
+base_model: allenai/longformer-base-4096
+tags:
+- generated_from_trainer
+datasets:
+- fancy_dataset
+metrics:
+- accuracy
+model-index:
+- name: longformer-spans
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: fancy_dataset
+      type: fancy_dataset
+      config: spans
+      split: test
+      args: spans
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9372962126912465
+---
+
+<!-- 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. -->
+
+# longformer-spans
+
+This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the fancy_dataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1948
+- : {'precision': 0.9405594405594405, 'recall': 0.9672104754749383, 'f1-score': 0.9536988041062546, 'support': 18634.0}
+- O: {'precision': 0.9301474791357037, 'recall': 0.8771967654986523, 'f1-score': 0.9028964598823661, 'support': 9275.0}
+- Accuracy: 0.9373
+- Macro avg: {'precision': 0.9353534598475721, 'recall': 0.9222036204867954, 'f1-score': 0.9282976319943104, 'support': 27909.0}
+- Weighted avg: {'precision': 0.9370992326621616, 'recall': 0.9372962126912465, 'f1-score': 0.9368156573551505, 'support': 27909.0}
+
+## 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 |                                                                                                                     | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.2901          | {'precision': 0.9357719203873051, 'recall': 0.9335623054631319, 'f1-score': 0.9346658070062326, 'support': 18634.0} | {'precision': 0.8671531280180277, 'recall': 0.871266846361186, 'f1-score': 0.8692051199311606, 'support': 9275.0}  | 0.9129   | {'precision': 0.9014625242026664, 'recall': 0.9024145759121589, 'f1-score': 0.9019354634686966, 'support': 27909.0} | {'precision': 0.9129678321281397, 'recall': 0.9128596510086352, 'f1-score': 0.9129112521091997, 'support': 27909.0} |
+| No log        | 2.0   | 82   | 0.2109          | {'precision': 0.9311551324929251, 'recall': 0.9711817108511324, 'f1-score': 0.9507473272216239, 'support': 18634.0} | {'precision': 0.9366296908189757, 'recall': 0.8557412398921833, 'f1-score': 0.8943602456476422, 'support': 9275.0} | 0.9328   | {'precision': 0.9338924116559504, 'recall': 0.9134614753716579, 'f1-score': 0.922553786434633, 'support': 27909.0}  | {'precision': 0.9329744928596212, 'recall': 0.9328173707406213, 'f1-score': 0.9320082043007497, 'support': 27909.0} |
+| No log        | 3.0   | 123  | 0.1948          | {'precision': 0.9405594405594405, 'recall': 0.9672104754749383, 'f1-score': 0.9536988041062546, 'support': 18634.0} | {'precision': 0.9301474791357037, 'recall': 0.8771967654986523, 'f1-score': 0.9028964598823661, 'support': 9275.0} | 0.9373   | {'precision': 0.9353534598475721, 'recall': 0.9222036204867954, 'f1-score': 0.9282976319943104, 'support': 27909.0} | {'precision': 0.9370992326621616, 'recall': 0.9372962126912465, 'f1-score': 0.9368156573551505, 'support': 27909.0} |
+
+
+### Framework versions
+
+- Transformers 4.37.1
+- Pytorch 2.1.2+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.1

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