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test-full_labels
Theoreticallyhugo/test-full_labels
test-full_labels is a token classification model from Theoreticallyhugo. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- gpt2
- Access
- Public
- Created
- Mar 3, 2024
- Updated
- Mar 3, 2024
- SHA
- d543e57f
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Datasets
Model card
From the Hugging Face model README
<!-- 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 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
- Task
- Token Classification
- Library
- transformers
- Type
- gpt2
- License
- mit
- Created
- Mar 3, 2024
- Updated
- Mar 3, 2024