metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: IKT_classifier_netzero_best
results: []
widget:
- text: >-
"We have put forth a long-term low- emissions development strategy (LEDS)
that aspires to halve emissions from its peak to 33 MtCO2e by 2050, with a
view to achieving net-zero emissions as soon as viable in the second half
of the century. This will require serious and concerted efforts across our
industry, economy and society"
example_title: NET-ZERO
- text: >-
"Unconditional Contribution In the unconditional scenario, GHG emissions
would be reduced by 27.56 Mt CO2e (6.73%) below BAU in 2030 in the
respective sectors. 26.3 Mt CO2e (95.4%) of this emission reduction will
be from the Energy sector while 0.64 (2.3%) and 0.6 (2.2%) Mt CO2e
reduction will be from AFOLU (agriculture) and waste sector respectively.
There will be no reduction in the IPPU sector. Conditional Contribution In
the conditional scenario, GHG emissions would be reduced by 61.9 Mt CO2e
(15.12%) below BAU in 2030 in the respective sectors."
example_title: TARGET_FREE
- text: >-
"This land is buffered from the sea by the dyke and a network of drains
and pumps will control the water levels in the polder. We have raised the
minimum platform levels for new developments from 3m to 4m above the
Singapore Height Datum (SHD) since 2011. Presently, critical
infrastructure on existing coastal land, notably Changi Airport Terminal 5
and Tuas Port, will be constructed with platform levels at least 5m above
SHD."
example_title: NEGATIVE
IKT_classifier_netzero_best
This model is a fine-tuned version of sentence-transformers/all-mpnet-base-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9526
- Precision Macro: 0.7813
- Precision Weighted: 0.8164
- Recall Macro: 0.7734
- Recall Weighted: 0.7812
- F1-score: 0.7644
- Accuracy: 0.7812
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: 9.588722322096848e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400.0
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision Macro | Precision Weighted | Recall Macro | Recall Weighted | F1-score | Accuracy |
|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 114 | 0.8267 | 0.8056 | 0.8151 | 0.6601 | 0.6875 | 0.6418 | 0.6875 |
| No log | 2.0 | 228 | 0.4916 | 0.8095 | 0.8371 | 0.8290 | 0.8125 | 0.8113 | 0.8125 |
| No log | 3.0 | 342 | 0.4784 | 0.8535 | 0.8920 | 0.8682 | 0.875 | 0.8569 | 0.875 |
| No log | 4.0 | 456 | 0.8909 | 0.7813 | 0.8164 | 0.7734 | 0.7812 | 0.7644 | 0.7812 |
| 0.6167 | 5.0 | 570 | 0.6673 | 0.8242 | 0.8650 | 0.8649 | 0.8125 | 0.8260 | 0.8125 |
| 0.6167 | 6.0 | 684 | 0.7110 | 0.8413 | 0.8795 | 0.8845 | 0.8438 | 0.8505 | 0.8438 |
| 0.6167 | 7.0 | 798 | 1.3731 | 0.7778 | 0.8281 | 0.7702 | 0.7188 | 0.7380 | 0.7188 |
| 0.6167 | 8.0 | 912 | 0.9526 | 0.7813 | 0.8164 | 0.7734 | 0.7812 | 0.7644 | 0.7812 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3