update model card README.md

README.md

@@ -1,49 +1,60 @@
 ---
 license: apache-2.0
 tags:
-- setfit
-- sentence-transformers
-- text-classification
-pipeline_tag: text-classification
+- generated_from_trainer
+datasets:
+- squad
+model-index:
+- name: li-tis-tuned-2
+  results: []
 ---
 
-# alexkueck/li-tis-tuned-2
+<!-- 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:
+# li-tis-tuned-2
 
-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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.7269
 
-## 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("alexkueck/li-tis-tuned-2")
-# 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| No log        | 1.0   | 250  | 2.7023          |
+| 2.8709        | 2.0   | 500  | 1.8528          |
+| 2.8709        | 3.0   | 750  | 1.7269          |
+
+
+### Framework versions
+
+- Transformers 4.29.1
+- Pytorch 2.0.1+cu117
+- Datasets 2.14.4
+- Tokenizers 0.13.3