trainer: training complete at 2024-03-03 17:42:24.016498.

README.md

@@ -0,0 +1,88 @@
+---
+license: mit
+base_model: openai-community/gpt2
+tags:
+- generated_from_trainer
+datasets:
+- essays_su_g
+metrics:
+- accuracy
+model-index:
+- name: test-full_labels
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: essays_su_g
+      type: essays_su_g
+      config: full_labels
+      split: train[0%:20%]
+      args: full_labels
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.7248585259425923
+---
+
+<!-- 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. -->
+
+# test-full_labels
+
+This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the essays_su_g dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7319
+- B-claim: {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 284.0}
+- B-majorclaim: {'precision': 0.2, 'recall': 0.028368794326241134, 'f1-score': 0.04968944099378882, 'support': 141.0}
+- B-premise: {'precision': 0.5708502024291497, 'recall': 0.19915254237288135, 'f1-score': 0.29528795811518327, 'support': 708.0}
+- I-claim: {'precision': 0.38082266412421684, 'recall': 0.3428991905813098, 'f1-score': 0.3608673205988642, 'support': 4077.0}
+- I-majorclaim: {'precision': 0.5423883318140383, 'recall': 0.2939723320158103, 'f1-score': 0.38128804870233907, 'support': 2024.0}
+- I-premise: {'precision': 0.7635793871866295, 'recall': 0.8964192282537606, 'f1-score': 0.8246841155234658, 'support': 12232.0}
+- O: {'precision': 0.8210081497132509, 'recall': 0.8269152817186867, 'f1-score': 0.8239511283889535, 'support': 9868.0}
+- Accuracy: 0.7249
+- Macro avg: {'precision': 0.4683783907524693, 'recall': 0.3696753384669557, 'f1-score': 0.3908240017603707, 'support': 29334.0}
+- Weighted avg: {'precision': 0.6996857371644881, 'recall': 0.7248585259425923, 'f1-score': 0.7048932637283017, 'support': 29334.0}
+
+## 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | B-claim                                                                                                 | B-majorclaim                                                                                                         | B-premise                                                                                                           | I-claim                                                                                                                   | I-majorclaim                                                                                                          | I-premise                                                                                                           | O                                                                                                                  | Accuracy | Macro avg                                                                                                             | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 1.1499          | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 284.0}                                    | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 141.0}                                                 | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 708.0}                                                | {'precision': 0.03305785123966942, 'recall': 0.0009811135638950208, 'f1-score': 0.0019056693663649356, 'support': 4077.0} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 2024.0}                                                 | {'precision': 0.5419036077188403, 'recall': 0.9504578155657293, 'f1-score': 0.6902570800926201, 'support': 12232.0} | {'precision': 0.7500322622273842, 'recall': 0.5889744629104176, 'f1-score': 0.6598172220014759, 'support': 9868.0} | 0.5946   | {'precision': 0.18928481731227054, 'recall': 0.22005905600572026, 'f1-score': 0.193139995922923, 'support': 29334.0}  | {'precision': 0.4828751671364564, 'recall': 0.5946001227244835, 'f1-score': 0.5100589883551566, 'support': 29334.0} |
+| No log        | 2.0   | 82   | 0.8679          | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 284.0}                                    | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 141.0}                                                 | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 708.0}                                                | {'precision': 0.2435064935064935, 'recall': 0.03679175864606328, 'f1-score': 0.06392499467291711, 'support': 4077.0}      | {'precision': 0.3858267716535433, 'recall': 0.04841897233201581, 'f1-score': 0.08604038630377524, 'support': 2024.0}  | {'precision': 0.648795078729048, 'recall': 0.9398299542184434, 'f1-score': 0.7676538345965076, 'support': 12232.0}  | {'precision': 0.7703193371194489, 'recall': 0.8384677746250506, 'f1-score': 0.8029501674025912, 'support': 9868.0} | 0.6824   | {'precision': 0.29263538300121905, 'recall': 0.2662154942602247, 'f1-score': 0.24579562613939873, 'support': 29334.0} | {'precision': 0.5901432461158105, 'recall': 0.6824163087202564, 'f1-score': 0.605039268489588, 'support': 29334.0}  |
+| No log        | 3.0   | 123  | 0.7978          | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 284.0}                                    | {'precision': 0.125, 'recall': 0.0070921985815602835, 'f1-score': 0.013422818791946307, 'support': 141.0}            | {'precision': 0.6071428571428571, 'recall': 0.07203389830508475, 'f1-score': 0.12878787878787878, 'support': 708.0} | {'precision': 0.31603053435114503, 'recall': 0.304635761589404, 'f1-score': 0.3102285500187336, 'support': 4077.0}        | {'precision': 0.5198019801980198, 'recall': 0.2075098814229249, 'f1-score': 0.2966101694915254, 'support': 2024.0}    | {'precision': 0.7561827382225073, 'recall': 0.8673969914977109, 'f1-score': 0.8079808095038647, 'support': 12232.0} | {'precision': 0.7885992552277284, 'recall': 0.8369477097689502, 'f1-score': 0.812054471264933, 'support': 9868.0}  | 0.7017   | {'precision': 0.44467962359175106, 'recall': 0.327945205880805, 'f1-score': 0.3384406711226974, 'support': 29334.0}   | {'precision': 0.6756508673843488, 'recall': 0.7016772346083043, 'f1-score': 0.6768524579464901, 'support': 29334.0} |
+| No log        | 4.0   | 164  | 0.7564          | {'precision': 1.0, 'recall': 0.0035211267605633804, 'f1-score': 0.007017543859649122, 'support': 284.0} | {'precision': 0.2857142857142857, 'recall': 0.028368794326241134, 'f1-score': 0.05161290322580645, 'support': 141.0} | {'precision': 0.5414634146341464, 'recall': 0.15677966101694915, 'f1-score': 0.24315443592552025, 'support': 708.0} | {'precision': 0.3514654161781946, 'recall': 0.36767230806965906, 'f1-score': 0.3593862383121553, 'support': 4077.0}       | {'precision': 0.49387755102040815, 'recall': 0.29891304347826086, 'f1-score': 0.37242228377962444, 'support': 2024.0} | {'precision': 0.7616845350711232, 'recall': 0.8886527141922825, 'f1-score': 0.8202844960947816, 'support': 12232.0} | {'precision': 0.8391959798994975, 'recall': 0.795399270368869, 'f1-score': 0.8167108891316789, 'support': 9868.0}  | 0.7138   | {'precision': 0.610485883216808, 'recall': 0.3627581311732607, 'f1-score': 0.38151268433274516, 'support': 29334.0}   | {'precision': 0.7069709429163672, 'recall': 0.7138133224244904, 'f1-score': 0.6986243658756951, 'support': 29334.0} |
+| No log        | 5.0   | 205  | 0.7319          | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 284.0}                                    | {'precision': 0.2, 'recall': 0.028368794326241134, 'f1-score': 0.04968944099378882, 'support': 141.0}                | {'precision': 0.5708502024291497, 'recall': 0.19915254237288135, 'f1-score': 0.29528795811518327, 'support': 708.0} | {'precision': 0.38082266412421684, 'recall': 0.3428991905813098, 'f1-score': 0.3608673205988642, 'support': 4077.0}       | {'precision': 0.5423883318140383, 'recall': 0.2939723320158103, 'f1-score': 0.38128804870233907, 'support': 2024.0}   | {'precision': 0.7635793871866295, 'recall': 0.8964192282537606, 'f1-score': 0.8246841155234658, 'support': 12232.0} | {'precision': 0.8210081497132509, 'recall': 0.8269152817186867, 'f1-score': 0.8239511283889535, 'support': 9868.0} | 0.7249   | {'precision': 0.4683783907524693, 'recall': 0.3696753384669557, 'f1-score': 0.3908240017603707, 'support': 29334.0}   | {'precision': 0.6996857371644881, 'recall': 0.7248585259425923, 'f1-score': 0.7048932637283017, 'support': 29334.0} |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

meta_data/README_s42_e5.md

@@ -0,0 +1,88 @@
+---
+license: mit
+base_model: openai-community/gpt2
+tags:
+- generated_from_trainer
+datasets:
+- essays_su_g
+metrics:
+- accuracy
+model-index:
+- name: test-full_labels
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: essays_su_g
+      type: essays_su_g
+      config: full_labels
+      split: train[0%:20%]
+      args: full_labels
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.7248585259425923
+---
+
+<!-- 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. -->
+
+# test-full_labels
+
+This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the essays_su_g dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7319
+- B-claim: {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 284.0}
+- B-majorclaim: {'precision': 0.2, 'recall': 0.028368794326241134, 'f1-score': 0.04968944099378882, 'support': 141.0}
+- B-premise: {'precision': 0.5708502024291497, 'recall': 0.19915254237288135, 'f1-score': 0.29528795811518327, 'support': 708.0}
+- I-claim: {'precision': 0.38082266412421684, 'recall': 0.3428991905813098, 'f1-score': 0.3608673205988642, 'support': 4077.0}
+- I-majorclaim: {'precision': 0.5423883318140383, 'recall': 0.2939723320158103, 'f1-score': 0.38128804870233907, 'support': 2024.0}
+- I-premise: {'precision': 0.7635793871866295, 'recall': 0.8964192282537606, 'f1-score': 0.8246841155234658, 'support': 12232.0}
+- O: {'precision': 0.8210081497132509, 'recall': 0.8269152817186867, 'f1-score': 0.8239511283889535, 'support': 9868.0}
+- Accuracy: 0.7249
+- Macro avg: {'precision': 0.4683783907524693, 'recall': 0.3696753384669557, 'f1-score': 0.3908240017603707, 'support': 29334.0}
+- Weighted avg: {'precision': 0.6996857371644881, 'recall': 0.7248585259425923, 'f1-score': 0.7048932637283017, 'support': 29334.0}
+
+## 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | B-claim                                                                                                 | B-majorclaim                                                                                                         | B-premise                                                                                                           | I-claim                                                                                                                   | I-majorclaim                                                                                                          | I-premise                                                                                                           | O                                                                                                                  | Accuracy | Macro avg                                                                                                             | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 1.1499          | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 284.0}                                    | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 141.0}                                                 | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 708.0}                                                | {'precision': 0.03305785123966942, 'recall': 0.0009811135638950208, 'f1-score': 0.0019056693663649356, 'support': 4077.0} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 2024.0}                                                 | {'precision': 0.5419036077188403, 'recall': 0.9504578155657293, 'f1-score': 0.6902570800926201, 'support': 12232.0} | {'precision': 0.7500322622273842, 'recall': 0.5889744629104176, 'f1-score': 0.6598172220014759, 'support': 9868.0} | 0.5946   | {'precision': 0.18928481731227054, 'recall': 0.22005905600572026, 'f1-score': 0.193139995922923, 'support': 29334.0}  | {'precision': 0.4828751671364564, 'recall': 0.5946001227244835, 'f1-score': 0.5100589883551566, 'support': 29334.0} |
+| No log        | 2.0   | 82   | 0.8679          | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 284.0}                                    | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 141.0}                                                 | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 708.0}                                                | {'precision': 0.2435064935064935, 'recall': 0.03679175864606328, 'f1-score': 0.06392499467291711, 'support': 4077.0}      | {'precision': 0.3858267716535433, 'recall': 0.04841897233201581, 'f1-score': 0.08604038630377524, 'support': 2024.0}  | {'precision': 0.648795078729048, 'recall': 0.9398299542184434, 'f1-score': 0.7676538345965076, 'support': 12232.0}  | {'precision': 0.7703193371194489, 'recall': 0.8384677746250506, 'f1-score': 0.8029501674025912, 'support': 9868.0} | 0.6824   | {'precision': 0.29263538300121905, 'recall': 0.2662154942602247, 'f1-score': 0.24579562613939873, 'support': 29334.0} | {'precision': 0.5901432461158105, 'recall': 0.6824163087202564, 'f1-score': 0.605039268489588, 'support': 29334.0}  |
+| No log        | 3.0   | 123  | 0.7978          | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 284.0}                                    | {'precision': 0.125, 'recall': 0.0070921985815602835, 'f1-score': 0.013422818791946307, 'support': 141.0}            | {'precision': 0.6071428571428571, 'recall': 0.07203389830508475, 'f1-score': 0.12878787878787878, 'support': 708.0} | {'precision': 0.31603053435114503, 'recall': 0.304635761589404, 'f1-score': 0.3102285500187336, 'support': 4077.0}        | {'precision': 0.5198019801980198, 'recall': 0.2075098814229249, 'f1-score': 0.2966101694915254, 'support': 2024.0}    | {'precision': 0.7561827382225073, 'recall': 0.8673969914977109, 'f1-score': 0.8079808095038647, 'support': 12232.0} | {'precision': 0.7885992552277284, 'recall': 0.8369477097689502, 'f1-score': 0.812054471264933, 'support': 9868.0}  | 0.7017   | {'precision': 0.44467962359175106, 'recall': 0.327945205880805, 'f1-score': 0.3384406711226974, 'support': 29334.0}   | {'precision': 0.6756508673843488, 'recall': 0.7016772346083043, 'f1-score': 0.6768524579464901, 'support': 29334.0} |
+| No log        | 4.0   | 164  | 0.7564          | {'precision': 1.0, 'recall': 0.0035211267605633804, 'f1-score': 0.007017543859649122, 'support': 284.0} | {'precision': 0.2857142857142857, 'recall': 0.028368794326241134, 'f1-score': 0.05161290322580645, 'support': 141.0} | {'precision': 0.5414634146341464, 'recall': 0.15677966101694915, 'f1-score': 0.24315443592552025, 'support': 708.0} | {'precision': 0.3514654161781946, 'recall': 0.36767230806965906, 'f1-score': 0.3593862383121553, 'support': 4077.0}       | {'precision': 0.49387755102040815, 'recall': 0.29891304347826086, 'f1-score': 0.37242228377962444, 'support': 2024.0} | {'precision': 0.7616845350711232, 'recall': 0.8886527141922825, 'f1-score': 0.8202844960947816, 'support': 12232.0} | {'precision': 0.8391959798994975, 'recall': 0.795399270368869, 'f1-score': 0.8167108891316789, 'support': 9868.0}  | 0.7138   | {'precision': 0.610485883216808, 'recall': 0.3627581311732607, 'f1-score': 0.38151268433274516, 'support': 29334.0}   | {'precision': 0.7069709429163672, 'recall': 0.7138133224244904, 'f1-score': 0.6986243658756951, 'support': 29334.0} |
+| No log        | 5.0   | 205  | 0.7319          | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 284.0}                                    | {'precision': 0.2, 'recall': 0.028368794326241134, 'f1-score': 0.04968944099378882, 'support': 141.0}                | {'precision': 0.5708502024291497, 'recall': 0.19915254237288135, 'f1-score': 0.29528795811518327, 'support': 708.0} | {'precision': 0.38082266412421684, 'recall': 0.3428991905813098, 'f1-score': 0.3608673205988642, 'support': 4077.0}       | {'precision': 0.5423883318140383, 'recall': 0.2939723320158103, 'f1-score': 0.38128804870233907, 'support': 2024.0}   | {'precision': 0.7635793871866295, 'recall': 0.8964192282537606, 'f1-score': 0.8246841155234658, 'support': 12232.0} | {'precision': 0.8210081497132509, 'recall': 0.8269152817186867, 'f1-score': 0.8239511283889535, 'support': 9868.0} | 0.7249   | {'precision': 0.4683783907524693, 'recall': 0.3696753384669557, 'f1-score': 0.3908240017603707, 'support': 29334.0}   | {'precision': 0.6996857371644881, 'recall': 0.7248585259425923, 'f1-score': 0.7048932637283017, 'support': 29334.0} |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

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