Update README.md

README.md

@@ -1,77 +1,72 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+datasets: 
+- uonlp/CulturaX
+- others
+language:
+- bg
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
+T5 model trained on Bulgarian literature, Web, and other datasets, tokenized on character level.
 
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+470M parameter T5 model trained on 10B words (54B characters) for 3 epochs with T5 Span Corruption objective on character level.
+- Tokenizer vocabulary size is 512.
+- Model hidden dimension is 1024.
+- Feed-Forward dimension is 4096.
+- Hidden layer count is 16 for both the encoder and the decoder.
+
+- **Developed by:** Artificial Inteligence and Language Technologies Department at [Institute of Information and Communication Technologies](https://www.iict.bas.bg/en/index.html) - Bulgarian Academy of Sciences.
+- **Funded by:** The model is pretrained within the [CLaDA-BG: National Interdisciplinary Research E-Infrastructure for
+Bulgarian Language and Cultural heritage - member of the pan-European research consortia CLARIN-ERIC & DARIAH-ERIC](https://clada-bg.eu/en/),
+funded by the Ministry of Education and Science of Bulgaria (support for the Bulgarian National Roadmap for Research Infrastructure).
+The training was performed at the supercomputer [HEMUS](http://ict.acad.bg/?page_id=1659) at IICT-BAS, part of the RIs of the CoE on Informatics and ICT, financed by the OP SESG (2014–2020), and co-financed by the European Union through the ESIF.
+- **Model type:** T5
+- **Language(s) (NLP):** Bulgarian.
+- **License:** MIT
 
 ## Uses
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+The model is intended to be used as a base model for fine-tuning tasks in NLP.
 
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+```python
+>>> import torch
+>>> from transformers import (
+>>>     T5ForConditionalGeneration,
+>>>     PreTrainedTokenizerFast
+>>> )
 
-[More Information Needed]
+>>> model = T5ForConditionalGeneration.from_pretrained('AIaLT-IICT/t5_char_bg_base')
+>>> tokenizer = PreTrainedTokenizerFast.from_pretrained('AIaLT-IICT/t5_char_bg_base')
 
-### Downstream Use [optional]
+>>> prompt = "Събудих се след[SEN_1]и отидох да си купя[SEN_2]."
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+>>> model_inputs = tokenizer([prompt], return_tensors="pt", add_special_tokens=True, return_token_type_ids=False)
+>>> generated_ids = model.generate(**model_inputs)
+>>> tokenizer.decode(generated_ids[0])
 
-[More Information Needed]
+'[CLS][SEN_1] полунощ [SEN_2] кола.[SEN_3][SEP]'
+```
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model is trained on span corruption task.
+If you want to use the model for any other type of text generation it is recommended to fine-tune it.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+It is recommended to use the model for text generation fine-tuning tasks that need character level modeling for example spelling correction.
+The encoder of the model alone can be used for text and token classification.
 
 ## Training Details
 
@@ -79,121 +74,29 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-[More Information Needed]
+Trained on 10B tokens consisting of deduplicated union of:
+- uonlp/CulturaX
+- [MaCoCu-bg 2.0](https://www.clarin.si/repository/xmlui/handle/11356/1800)
+- [HPLT 2.0 Bulgarian (Cyrillic) cleaned](https://data.hplt-project.org/two/cleaned/bul_Cyrl_map.txt)
+- Literature
+- Wikipedia
+- others
 
 ### Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
+Trained with the T5 Span Corruption objective with 25% noise density, 7 characters mean noise span length for 3 epochs with bf16 mixed precision, 1024 tokens input length and batch size of 256*1024 tokens.
 
 
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
 ## Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
+The model is evaluated on the T5 Span Corruption objective that it was trained on.
+It achieves test loss of 1.38 and test accuracy of 71.50%
 
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
 
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+## Model Card Authors
+Nikolay Paev, Kiril Simov
 
 ## Model Card Contact
-
-[More Information Needed]
+nikolay.paev@iict.bas.bg