Update README.md

README.md

@@ -10,15 +10,9 @@ widget:
  example_title: "1"
 ---
 
-<!-- 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 an experimental model that should fix your typos and punctuation.
+If you like to run your own experiments or train for a different language, have a look at [the code](https://github.com/oliverguhr/spelling).
 
-# bart-base-spelling-de
-
-This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1065
-- Cer: 0.2022
 
 ## Model description
 
@@ -26,8 +20,8 @@ This is a proof of concept spelling correction model for german.
 
 ## Intended uses & limitations
 
-This is work in progress, be aware that the model can produce artefacts. 
-You can test the model using the pipeline interface:
+This is a work in progress, be aware that the model can produce artefacts. 
+You can test the model using the pipeline-interface:
 
 ```python
 from transformers import pipeline
@@ -37,55 +31,3 @@ fix_spelling = pipeline("text2text-generation",model="oliverguhr/spelling-correc
 print(fix_spelling("das idst ein neuZr test",max_length=2048))
 ```
 
-
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.0003
-- train_batch_size: 2
-- eval_batch_size: 4
-- seed: 42
-- gradient_accumulation_steps: 16
-- total_train_batch_size: 32
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 2.0
-- mixed_precision_training: Native AMP
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Cer |
-|:-------------:|:-----:|:-----:|:---------------:|:------:|
-| 0.2803 | 0.11 | 1000 | 0.1978 | 0.9429 |
-| 0.1688 | 0.21 | 2000 | 0.1472 | 0.9426 |
-| 0.121 | 0.32 | 3000 | 0.1381 | 0.9424 |
-| 0.1722 | 0.43 | 4000 | 0.1340 | 0.9425 |
-| 0.1502 | 0.54 | 5000 | 0.1292 | 0.9423 |
-| 0.1556 | 0.64 | 6000 | 0.1260 | 0.9424 |
-| 0.1624 | 0.75 | 7000 | 0.1246 | 0.9425 |
-| 0.1337 | 0.86 | 8000 | 0.1213 | 0.9424 |
-| 0.131 | 0.96 | 9000 | 0.1195 | 0.9423 |
-| 0.1137 | 1.07 | 10000 | 0.1178 | 0.9424 |
-| 0.0958 | 1.18 | 11000 | 0.1166 | 0.9422 |
-| 0.1067 | 1.28 | 12000 | 0.1147 | 0.9422 |
-| 0.1201 | 1.39 | 13000 | 0.1135 | 0.9423 |
-| 0.1115 | 1.5 | 14000 | 0.1111 | 0.9423 |
-| 0.1284 | 1.61 | 15000 | 0.1101 | 0.9422 |
-| 0.0947 | 1.71 | 16000 | 0.1085 | 0.9422 |
-| 0.1081 | 1.82 | 17000 | 0.1073 | 0.9422 |
-| 0.099 | 1.93 | 18000 | 0.1065 | 0.9422 |
-
-
-### Framework versions
-
-- Transformers 4.19.0.dev0
-- Pytorch 1.11.0+cu102
-- Datasets 2.1.0
-- Tokenizers 0.12.1