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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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---
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# Model Card
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## Model Details
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###
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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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- **Language
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- **License:** MIT
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###
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### Model Sources
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###
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## Training Details
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[More Information Needed]
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#### Training Hyperparameters
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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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- **Cluster Provider:** [Artemisa](https://artemisa.ific.uv.es/web/)
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- **Compute Region:** EU
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- **Carbon Emitted:**
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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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# 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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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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## Bias, Risks, and Limitations
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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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## 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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