Upload TFMBartForConditionalGeneration

README.md

@@ -1,83 +1,47 @@
 ---
-language:
-- tr
-arXiv: 2403.01308
-library_name: transformers
-pipeline_tag: text2text-generation
-inference:
-  parameters:
-    max_new_tokens: 128
-widget:
-- text: >-
-    Ben buraya bazı <MASK> istiyorum.
-  example_title: Masked Language Modeling
-license: cc-by-nc-sa-4.0
+tags:
+- generated_from_keras_callback
+model-index:
+- name: VBART-Medium-Base
+  results: []
 ---
-# VBART Model Card
-
-## Model Description  
-
-VBART is the first sequence-to-sequence LLM pre-trained on Turkish corpora from scratch on a large scale. It was pre-trained by VNGRS in February 2023.  
-The model is capable of conditional text generation tasks such as text summarization, paraphrasing, and title generation when fine-tuned.
-It outperforms its multilingual counterparts, albeit being much smaller than other implementations.
-
-This repository contains pre-trained TensorFlow and Safetensors weights of VBARTMedium-Base.
-
-- **Developed by:** [VNGRS-AI](https://vngrs.com/ai/)
-- **Model type:**  Transformer encoder-decoder based on mBART architecture
-- **Language(s) (NLP):** Turkish
-- **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  
-The base model is pre-trained on [vngrs-web-corpus](https://huggingface.co/datasets/vngrs-ai/vngrs-web-corpus). It is curated by cleaning and filtering Turkish parts of [OSCAR-2201](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201) and [mC4](https://huggingface.co/datasets/mc4) datasets. These datasets consist of documents of unstructured web crawl data. More information about the dataset can be found on their respective pages. Data is filtered using a set of heuristics and certain rules, explained in the appendix of our [paper](https://arxiv.org/abs/2403.01308).
-
-### Limitations
-This model is the pre-trained base model and is capable of masked language modeling.
-Its purpose is to serve as the base model to be fine-tuned for downstream tasks.
-
-### Training Procedure  
-Pre-trained for a total of 63B tokens.
-#### Hardware
-- **GPUs**: 8 x Nvidia A100-80 GB
-#### Software
-- TensorFlow
-#### 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)
-- **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
-- **Initial Learning rate**: 5e-6
-- **Training tokens**: 63B
-
-
-## Citation  
-```
-@article{turker2024vbart,
-  title={VBART: The Turkish LLM},
-  author={Turker, Meliksah and Ari, Erdi and Han, Aydin},
-  journal={arXiv preprint arXiv:2403.01308},
-  year={2024}
-}
-```
+
+<!-- This model card has been generated automatically according to the information Keras had access to. You should
+probably proofread and complete it, then remove this comment. -->
+
+# VBART-Medium-Base
+
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+It achieves the following results on the evaluation set:
+
+
+## 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:
+- optimizer: None
+- training_precision: float32
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.39.0
+- TensorFlow 2.13.0
+- Datasets 2.18.0
+- Tokenizers 0.15.2

config.json

@@ -1,5 +1,4 @@
 {
-  "_name_or_path": "tfhf_model_checkpointmedium_epoch_0024_opt.hdf5",
   "activation_dropout": 0.0,
   "activation_function": "gelu",
   "architectures": [
@@ -28,8 +27,7 @@
   "num_hidden_layers": 6,
   "pad_token_id": 0,
   "scale_embedding": false,
-  "torch_dtype": "float32",
   "transformers_version": "4.39.0",
   "use_cache": true,
-  "vocab_size": 32000
+  "vocab_size": 32001
 }

generation_config.json

@@ -5,6 +5,5 @@
   "eos_token_id": 3,
   "forced_eos_token_id": 3,
   "pad_token_id": 0,
-  "transformers_version": "4.39.0",
-  "max_new_tokens": 128
+  "transformers_version": "4.39.0"
 }

tf_model.h5

@@ -1,3 +1,3 @@
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+size 502008588