Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use KingTechnician/roberta-base_Climate_LRTC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KingTechnician/roberta-base_Climate_LRTC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KingTechnician/roberta-base_Climate_LRTC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KingTechnician/roberta-base_Climate_LRTC") model = AutoModelForSequenceClassification.from_pretrained("KingTechnician/roberta-base_Climate_LRTC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +74 -0
- model.safetensors +1 -1
README.md
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---
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library_name: transformers
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license: mit
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base_model: roberta-base
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: roberta-base_Climate_LRTC
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# roberta-base_Climate_LRTC
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.3055
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- Accuracy: 0.3059
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- Macro Precision: 0.2071
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- Macro F1: 0.2024
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 12
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro Precision | Macro F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:--------:|
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| No log | 1.0 | 54 | 2.0799 | 0.2283 | 0.0285 | 0.0465 |
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| No log | 2.0 | 108 | 2.0098 | 0.2694 | 0.1451 | 0.1515 |
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| No log | 3.0 | 162 | 1.9431 | 0.1644 | 0.1181 | 0.1167 |
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| No log | 4.0 | 216 | 1.9499 | 0.1963 | 0.1930 | 0.1561 |
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| No log | 5.0 | 270 | 1.9525 | 0.2192 | 0.2342 | 0.1982 |
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| No log | 6.0 | 324 | 2.0069 | 0.2648 | 0.2232 | 0.2092 |
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| No log | 7.0 | 378 | 2.0393 | 0.2877 | 0.1995 | 0.1980 |
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| No log | 8.0 | 432 | 2.1675 | 0.3151 | 0.2590 | 0.2438 |
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| No log | 9.0 | 486 | 2.1879 | 0.3242 | 0.2509 | 0.2445 |
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| 1.4214 | 10.0 | 540 | 2.2435 | 0.2922 | 0.1962 | 0.1908 |
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| 1.4214 | 11.0 | 594 | 2.3314 | 0.3014 | 0.2107 | 0.2060 |
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| 1.4214 | 12.0 | 648 | 2.3055 | 0.3059 | 0.2071 | 0.2024 |
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### Framework versions
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- Transformers 5.0.0
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- Pytorch 2.10.0+cu128
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- Datasets 4.0.0
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- Tokenizers 0.22.2
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model.safetensors
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size 498631256
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version https://git-lfs.github.com/spec/v1
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size 498631256
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