Update README.md

README.md

@@ -4,14 +4,12 @@ language:
 arXiv: 2403.01308
 library_name: transformers
 pipeline_tag: text2text-generation
-inference:
-  parameters:
-    max_new_tokens: 128
 widget:
-- text: >-
-    Ben buraya bazı <MASK> istiyorum.
+- text: Ben buraya bazı <MASK> istiyorum.
   example_title: Masked Language Modeling
 license: cc-by-nc-sa-4.0
+datasets:
+- vngrs-ai/vngrs-web-corpus
 ---
 # VBART Model Card
 
@@ -29,23 +27,7 @@ This repository contains pre-trained TensorFlow and Safetensors weights of VBART
 - **License:** CC BY-NC-SA 4.0
 - **Finetuned from:** VBART-Large
 - **Paper:** [arXiv](https://arxiv.org/abs/2403.01308)
-## How to Get Started with the Model  
-```python
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-tokenizer = AutoTokenizer.from_pretrained("vngrs-ai/VBART-Medium-Base",
-                            model_input_names=['input_ids', 'attention_mask'])
-# Uncomment the device_map kwarg and delete the closing bracket to use model for inference on GPU
-model = AutoModelForSeq2SeqLM.from_pretrained("vngrs-ai/VBART-Medium-Base")#, device_map="auto")
-
-# Input text
-input_text = "Ben buraya bazı <MASK> istiyorum."
-
-token_input = tokenizer(input_text, return_tensors="pt")#.to('cuda')
-outputs = model.generate(**token_input)
-print(tokenizer.decode(outputs[0]))
-```
-  
 ## Training Details  
 
 ### Training Data  
@@ -64,7 +46,7 @@ Pre-trained for a total of 63B tokens.
 #### Hyperparameters  
 ##### Pretraining
 - **Training regime:** fp16 mixed precision
-- **Training objective**: Sentence permutation and span masking (using mask lengths sampled from Poisson distribution λ=3.5, masking 30% of tokens)
+- **Training objective**: Span masking (using mask lengths sampled from Poisson distribution λ=3.5, masking 30% of tokens)
 - **Optimizer** : Adam optimizer (β1 = 0.9, β2 = 0.98, Ɛ = 1e-6)
 - **Scheduler**: Custom scheduler from the original Transformers paper (20,000 warm-up steps)
 - **Dropout**: 0.1