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--- |
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library_name: transformers |
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language: en |
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license: apache-2.0 |
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datasets: |
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- stanfordnlp/imdb |
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base_model: |
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- google-bert/bert-base-uncased |
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--- |
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# Model Card: BERT-IMDb |
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An in-domain BERT-base model, 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 [BERT base (uncased)](https://huggingface.co/google-bert/bert-base-uncased) |
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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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Only the masked language modeling (MLM) objective was used during pre-training. |
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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:** BERT-base |
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- **Language:** English |
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- **License:** Apache 2.0 |
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- **Base model:** [BERT base model (uncased)](https://huggingface.co/google-bert/bert-base-uncased) |
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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 | 3516 | epoch-5 | step-3516 | |
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| 10 | 7033 | epoch-10 | step-7033 | |
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| 20 | 14066 | epoch-20 | step-14066 | |
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| 30 | 21100 | epoch-30 | step-21100 | |
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| 40 | 28133 | epoch-40 | step-28133 | |
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| 50 | 35166 | epoch-50 | step-35166 | |
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| 60 | 42200 | epoch-60 | step-42200 | |
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| 70 | 49233 | epoch-70 | step-49233 | |
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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 AutoModelForMaskedLM |
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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/google-bert/bert-base-uncased#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:** 32 |
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- **Gradient accumulation steps:** 3 |
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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/google-bert/bert-base-uncased#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/google-bert/bert-base-uncased#limitations-and-bias). |
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## Environmental Impact |
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- **Hardware Type:** NVIDIA Tesla V100 PCIE 32GB |
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- **Runtime:** 11.5 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:** 2.14 kg CO2 eq. |
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## Citation |
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**BibTeX:** |
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[More Information Needed] |
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