Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use alextsiak/climatebert-bin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alextsiak/climatebert-bin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alextsiak/climatebert-bin")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alextsiak/climatebert-bin") model = AutoModelForSequenceClassification.from_pretrained("alextsiak/climatebert-bin", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,217 Bytes
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library_name: transformers
license: apache-2.0
base_model: climatebert/distilroberta-base-climate-f
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: climatebert-bin
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. -->
# climatebert-bin
This model is a fine-tuned version of [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) on an unknown dataset.
It achieves the following results on the final test set:
- Loss: 0.548
- F1 Macro: 0.9156
- Accuracy: 0.9224
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 1.3366 | 1.0 | 17 | 1.2471 | 0.3958 | 0.6552 |
| 1.1692 | 2.0 | 34 | 1.0087 | 0.5175 | 0.6983 |
| 0.7289 | 3.0 | 51 | 0.4934 | 0.8916 | 0.9052 |
| 0.5135 | 4.0 | 68 | 0.4027 | 0.9343 | 0.9397 |
| 0.2444 | 5.0 | 85 | 0.3297 | 0.9237 | 0.9310 |
| 0.1078 | **6.0** | 102 | 0.3546 | **0.9428** | 0.9483 |
| 0.0307 | 7.0 | 119 | 0.5146 | 0.9254 | 0.9310 |
| 0.0193 | 8.0 | 136 | 0.5244 | 0.9336 | 0.9397 |
| 0.0277 | 9.0 | 153 | 0.8077 | 0.9076 | 0.9138 |
### Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
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