Instructions to use SpireLab/RESPIN_LanguageModels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SpireLab/RESPIN_LanguageModels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SpireLab/RESPIN_LanguageModels")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SpireLab/RESPIN_LanguageModels", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Delete train_data/muril_mt_domain
Browse files- train_data/muril_mt_domain/checkpoint-303/README.md +0 -202
- train_data/muril_mt_domain/checkpoint-303/adapter_config.json +0 -32
- train_data/muril_mt_domain/checkpoint-303/adapter_model.safetensors +0 -3
- train_data/muril_mt_domain/checkpoint-303/optimizer.pt +0 -3
- train_data/muril_mt_domain/checkpoint-303/rng_state.pth +0 -3
- train_data/muril_mt_domain/checkpoint-303/scheduler.pt +0 -3
- train_data/muril_mt_domain/checkpoint-303/trainer_state.json +0 -54
- train_data/muril_mt_domain/checkpoint-303/training_args.bin +0 -3
- train_data/muril_mt_domain/config.json +0 -26
- train_data/muril_mt_domain/generation_config.json +0 -5
- train_data/muril_mt_domain/model.safetensors +0 -3
- train_data/muril_mt_domain/special_tokens_map.json +0 -7
- train_data/muril_mt_domain/tokenizer.json +0 -0
- train_data/muril_mt_domain/tokenizer_config.json +0 -58
- train_data/muril_mt_domain/vocab.txt +0 -0
train_data/muril_mt_domain/checkpoint-303/README.md
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---
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base_model: google/muril-base-cased
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library_name: peft
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# Model Card for Model ID
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## Model Details
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### Model Description
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- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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## Training Details
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### Training Data
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### Results
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#### Summary
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## Environmental Impact
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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).
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- **Hardware Type:** [More Information Needed]
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#### Software
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## Citation [optional]
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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### Framework versions
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- PEFT 0.13.2
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size 14244
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train_data/muril_mt_domain/tokenizer_config.json
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