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  library_name: transformers
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  language: en
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  license: mit
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- datasets: []
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- tags: []
 
 
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  ---
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- # Model Card for <Model>
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- A pretrained GPT2 using <Dataset>.
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  ## Model Details
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- ### Model Description
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- A pretrained GPT2 using <Dataset>.
 
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  - **Developed by:** [Cesar Gonzalez-Gutierrez](https://ceguel.es)
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  - **Funded by:** [ERC](https://erc.europa.eu)
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- - **Model type:** pretrained GPT2
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- - **Language(s) (NLP):** English
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  - **License:** MIT
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- - **Pretrained from model:** [GPT2](https://huggingface.co/openai-community/gpt2)
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- ### Model Checkpoints
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- [More Information Needed]
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-
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- ### Model Sources
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- - **Paper:** [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
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- ## Intended Uses & Limitations
 
 
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- See <https://huggingface.co/openai-community/gpt2#intended-uses--limitations>.
 
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- ### Loading Checkpoints
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- [More Information Needed]
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  ## Training Details
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- ### Training Data
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-
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- [More Information Needed]
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- #### Preprocessing [optional]
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- [More Information Needed]
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  #### Training Hyperparameters
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- - **Training regime:** fp16
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  - **Batch size:** 8
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  - **Gradient accumulation steps:** 12
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  ## Environmental Impact
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  - **Hardware Type:** NVIDIA A100 PCIE 40GB
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- - **Hours used:** [More Information Needed]
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  - **Cluster Provider:** [Artemisa](https://artemisa.ific.uv.es/web/)
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  - **Compute Region:** EU
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- - **Carbon Emitted:** [More Information Needed] <!-- 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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  ## Citation
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  library_name: transformers
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  language: en
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  license: mit
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+ datasets:
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+ - stanfordnlp/imdb
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+ base_model:
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+ - openai-community/gpt2
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  ---
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+ # Model Card: GPT-2-IMDb
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+ An in-domain GPT-2, pre-trained from scratch on the IMDb dataset text.
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  ## Model Details
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+ ### Description
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+ This model is based on the [GPT-2](https://huggingface.co/openai-community/gpt2)
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+ architecture and was pre-trained from scratch (in-domain) using the text in IMDb dataset, excluding its test split.
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  - **Developed by:** [Cesar Gonzalez-Gutierrez](https://ceguel.es)
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  - **Funded by:** [ERC](https://erc.europa.eu)
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+ - **Architecture:** GPT-2
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+ - **Language:** English
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  - **License:** MIT
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+ - **Base model:** [GPT-2](https://huggingface.co/openai-community/gpt2)
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+ ### Checkpoints
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+ Intermediate checkpoints from the pre-training process are available and can be accessed using specific tags,
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+ which correspond to training epochs and steps:
 
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+ | Epoch | Step | Tags | |
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+ |---|---|---|---|
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+ | 1 | 703 | epoch-1 | step-703 |
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+ | 5 | 3515 | epoch-5 | step-3515 |
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+ | 10 | 7031 | epoch-10 | step-7031 |
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+ | 20 | 14063 | epoch-20 | step-14063 |
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+ | 30 | 21095 | epoch-30 | step-21095 |
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+ | 40 | 28126 | epoch-40 | step-28126 |
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+ | 50 | 35158 | epoch-50 | step-35158 |
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+ | 60 | 42190 | epoch-60 | step-42190 |
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+ | 70 | 49221 | epoch-70 | step-49221 |
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+ | 80 | 56240 | epoch-80 | step-56240 |
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+ To load a model from a specific intermediate checkpoint, use the `revision` parameter with the corresponding tag:
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+ ```python
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+ from transformers import AutoModelForCausalLM
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+ model = AutoModelForMaskedLM.from_pretrained("<model-name>", revision="<checkpoint-tag>")
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+ ```
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+ ### Sources
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+ - **Paper:** [Information pending]
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  ## Training Details
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+ For more details on the training procedure, please refer to the base model's documentation:
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+ [Training procedure](https://huggingface.co/openai-community/gpt2#training-procedure).
 
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+ ### Training Data
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+ All texts from IMDb dataset, excluding the test partition.
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  #### Training Hyperparameters
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+ - **Precision:** fp16
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  - **Batch size:** 8
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  - **Gradient accumulation steps:** 12
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+ ## Uses
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+
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+ For typical use cases and limitations, please refer to the base model's guidance:
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+ [Inteded uses & limitations](https://huggingface.co/openai-community/gpt2#intended-uses--limitations).
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+
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+ ## Bias, Risks, and Limitations
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+
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+ This model inherits potential risks and limitations from the base model. Refer to:
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+ [Limitations and bias](https://huggingface.co/openai-community/gpt2#limitations-and-bias).
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+
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  ## Environmental Impact
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  - **Hardware Type:** NVIDIA A100 PCIE 40GB
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+ - **Runtime:** 7 h
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  - **Cluster Provider:** [Artemisa](https://artemisa.ific.uv.es/web/)
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  - **Compute Region:** EU
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+ - **Carbon Emitted:** 1.08 kg CO2 eq.
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  ## Citation
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