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library_name: transformers
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##
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### Model Description
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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###
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[
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### Results
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<!-- Relevant interpretability work for the model goes here -->
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications
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### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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---
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library_name: transformers
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license: apache-2.0
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datasets:
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- isek-ai/danbooru-tags-2023
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# Dart (Danbooru Tags Transformer) v1
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This model is a pretrained Dart (**Da**nboo**r**u **T**ags Transformer) model that generates danbooru tags.
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Demo: [🤗 Space](https://huggingface.co/spaces/p1atdev/danbooru-tags-transformer)
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If you are an end user, it's recommended using the fine-tuned version, [p1atdev/dart-v1-sft](https://huggingface.co/p1atdev/dart-v1-sft), instead
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## Usage
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#### Note
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Since this model was trained only in alphabetical order, **placing tags that are later in alphabetical order at the beginning can prevent it from generating tags appropriately**.
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Using the [fine-tuned version]((https://huggingface.co/p1atdev/dart-v1-sft)) can eliminate this concern.
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### Using AutoModel
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🤗 Transformers library is required.
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```bash
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pip install -U transformers
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```
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```py
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
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MODEL_NAME = "p1atdev/dart-v1-base"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True) # trust_remote_code is required for tokenizer
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model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.bfloat16)
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prompt = "<|bos|><rating>rating:sfw, rating:general</rating><copyright>original</copyright><character></character><general>1girl, "
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inputs = tokenizer(prompt, return_tensors="pt").input_ids
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with torch.no_grad():
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outputs = model.generate(inputs, generation_config=generation_config)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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# rating:sfw, rating:general, original, 1girl, ahoge, black hair, blue eyes, blush, closed mouth, ear piercing, earrings, jewelry, looking at viewer, mole, mole under eye, piercing, portrait, shirt, short hair, solo, white shirt
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```
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#### Flash attention (optional)
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Using flash attention can optimize computations, but it is currently only compatible with Linux.
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```bash
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pip install flash_attn
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```
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### Accelerate with ORTModel
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🤗 Optimum library is also compatible, for the high performance inference using ONNX.
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```bash
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pip install "optimum[onnxruntime]"
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```
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Two ONNX models are provided:
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- [Normal](./model.onnx)
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- [Quantized](./model_quantized.onnx)
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Both can be utilized based on the following code:
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```py
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import torch
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from transformers import AutoTokenizer, GenerationConfig
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from optimum.onnxruntime import ORTModelForCausalLM
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MODEL_NAME = "p1atdev/dart-v1-base"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
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# normal version
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ort_model = ORTModelForCausalLM.from_pretrained(MODEL_NAME)
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# qunatized version
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# ort_model = ORTModelForCausalLM.from_pretrained(MODEL_NAME, file_name="model_quantized.onnx")
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prompt = "<|bos|><rating>rating:sfw, rating:general</rating><copyright>original</copyright><character></character><general>1girl, "
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inputs = tokenizer(prompt, return_tensors="pt").input_ids
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with torch.no_grad():
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outputs = model.generate(inputs, generation_config=generation_config)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### Prompt guidde
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Due to training with a specialized prompt format, **natural language is not supported**.
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The trained sentences are essentially composed of the following elements, arranged in the strict order shown below:
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- `<|bos|>`: The bos (begin of sentence) token
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- `<rating>[RATING_PARENT], [RATING_CHILD]</rating>`: The block of rating tags
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- [RATING_PARENT]: `rating:sfw`, `rating:nsfw`
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- [RATING_CHILD]:
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- if `[RATING_PARENT]` is `rating:sfw`: `rating:general`, `rating:sensitive`
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- else: `rating:questionable`, `rating:explicit`
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- `<copyright>[COPYRIGHT, ...]</copyright>`: The block of copyright tags.
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- [COPYRIGHT, ...]: All supported copyright tags can be seen in [TODO]()
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- `<character>[CHARACTER, ...]</character>`: The block of character tags.
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- [CHARACTER, ...]: All supported character tags can be seen in [TODO]()
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- `<general>[GENERAL, ...]</general>`: The block of general tags.
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- [GENERAL, ...]: All supported general tags can be seen in [TODO]()
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- `<|eos|>`: The eos (end of sentence) token
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- Tags other than special tokens are separated by commas.
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- All tags are arranged in alphabetical order.
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Example sentence:
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```
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<|bos|><rating>rating:sfw, rating:general</rating><copyright>vocaloid</copyright><character>hatsune miku</character><general>1girl, blue hair, cowboy shot, ...</general><|eos|>
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```
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Therefore, to complete the tags, the input prompt should be as follows:
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1. without any copyright and character tags
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```
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<|bos|><rating>rating:sfw, rating:general</rating><copyright></copyright><character></character><general>1girl
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```
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2. specifing copyright and character tags
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```
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<|bos|><rating>rating:sfw, rating:general</rating><copyright>sousou no frieren</copyright><character>frieren</character><general>1girl
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```
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## Model Details
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### Model Description
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- **Developed by:** Plat
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- **Model type:** Causal language model
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- **Language(s) (NLP):** Danbooru tags
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- **License:** Apache-2.0
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- **Demo:** Avaiable on [🤗Space](https://huggingface.co/spaces/p1atdev/danbooru-tags-transformer)
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## Bias, Risks, and Limitations
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Since this model is a pre-trained model, it cannot accommodate flexible specifications.
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## Training Details
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### Training Data
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This model was trained with:
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- [isek-ai/danbooru-tags-2023](https://huggingface.co/datasets/isek-ai/danbooru-tags-2023): 6M size of danbooru tags dataset since 2005 to 2023
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### Training Procedure
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Trained using 🤗 transformers' trainer.
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#### Preprocessing
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Preprocessing was conducted through the following process:
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1. Remove data where `general` tags is null.
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2. Remove `general` tags that appear less than 100 times.
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3. Remove undesirable tags such as `watermark` and `bad anatomy`.
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4. Remove based on the number of tags attached to a single post (following rules):
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- Remove if more than 100 for `general` tags.
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- Remove if more than 5 for `copyright` tags.
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- Remove if more than 10 for `character` tags.
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5. Concatenate while splitting with special tokens according to the category of the tags.
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#### Training Hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 32
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 64
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 1
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## Evaluation
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Evaluation has not been done yet and it needs to evaluate.
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## Technical Specifications
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### Model Architecture and Objective
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The architecture of this model is [OPT (Open Pretrained Transformer)](https://huggingface.co/docs/transformers/model_doc/opt), but the position embeddings was not trained.
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### Compute Infrastructure
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In house
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#### Hardware
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1x RTX 3070 Ti
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#### Software
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- Dataset processing: [🤗 Datasets](https://github.com/huggingface/datasets)
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- Training: [🤗 Transformers](https://github.com/huggingface/transformers)
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- Optimizing: [🤗 Optimum](https://github.com/huggingface/optimum)
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## More Information [optional]
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[More Information Needed]
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