update model card README.md

README.md

@@ -1,49 +1,72 @@
 ---
 license: apache-2.0
+base_model: sentence-transformers/all-mpnet-base-v2
 tags:
-- setfit
-- sentence-transformers
-- text-classification
-pipeline_tag: text-classification
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: IKT_classifier_mitigation_best
+  results: []
 ---
 
-# mtyrrell/IKT_classifier_mitigation_best
+<!-- 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. -->
 
-This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
+# IKT_classifier_mitigation_best
 
-1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
-2. Training a classification head with features from the fine-tuned Sentence Transformer.
+This model is a fine-tuned version of [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0515
+- Precision Micro: 0.2570
+- Precision Weighted: 0.2809
+- Precision Samples: 0.2896
+- Recall Micro: 0.6815
+- Recall Weighted: 0.6815
+- Recall Samples: 0.7119
+- F1-score: 0.3907
+- Accuracy: 0.0095
 
-## Usage
+## Model description
 
-To use this model for inference, first install the SetFit library:
+More information needed
 
-```bash
-python -m pip install setfit
-```
+## Intended uses & limitations
 
-You can then run inference as follows:
+More information needed
 
-```python
-from setfit import SetFitModel
+## Training and evaluation data
 
-# Download from Hub and run inference
-model = SetFitModel.from_pretrained("mtyrrell/IKT_classifier_mitigation_best")
-# Run inference
-preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
-```
+More information needed
 
-## BibTeX entry and citation info
+## Training procedure
 
-```bibtex
-@article{https://doi.org/10.48550/arxiv.2209.11055,
-doi = {10.48550/ARXIV.2209.11055},
-url = {https://arxiv.org/abs/2209.11055},
-author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
-keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
-title = {Efficient Few-Shot Learning Without Prompts},
-publisher = {arXiv},
-year = {2022},
-copyright = {Creative Commons Attribution 4.0 International}
-}
-```
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3.6181464293180716e-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: 300.0
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision Micro | Precision Weighted | Precision Samples | Recall Micro | Recall Weighted | Recall Samples | F1-score | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:---------------:|:--------------:|:--------:|:--------:|
+| No log        | 1.0   | 313  | 1.2909          | 0.1858          | 0.2078             | 0.1957            | 0.7185       | 0.7185          | 0.7222         | 0.2977   | 0.0      |
+| 1.262         | 2.0   | 626  | 1.0875          | 0.2099          | 0.2605             | 0.2295            | 0.7852       | 0.7852          | 0.8071         | 0.3431   | 0.0      |
+| 1.262         | 3.0   | 939  | 1.0171          | 0.2284          | 0.2612             | 0.2539            | 0.7630       | 0.7630          | 0.7746         | 0.3643   | 0.0095   |
+| 1.0059        | 4.0   | 1252 | 1.0510          | 0.2519          | 0.2764             | 0.2914            | 0.7259       | 0.7259          | 0.7563         | 0.4013   | 0.0095   |
+| 0.8421        | 5.0   | 1565 | 1.0515          | 0.2570          | 0.2809             | 0.2896            | 0.6815       | 0.6815          | 0.7119         | 0.3907   | 0.0095   |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.13.1
+- Tokenizers 0.13.3