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--- |
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license: other |
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base_model: facebook/opt-350m |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: tmp_trainer |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# tmp_trainer |
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This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the [addressWithContext](https://huggingface.co/datasets/piazzola/addressWithContext) dataset. |
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## Model description |
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**Make sure to set max_new_tokens = 20; otherwise, the model will generate one token at a time.** |
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nlp = pipeline("text-generation", |
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model="piazzola/tmp_trainer", |
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max_new_tokens=20) |
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nlp("Landlord hereby demises unto Tenant, and Tenant hereby leases from Landlord for the terms and upon the conditions set forth in this Lease 30,600 square feet of space in the building located at 15 Firstfield Road, Gaithersburg, Maryland, the Building, as set forth on Exhibit A, hereto attached, said space being referred to as the Premises.") |
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## Intended uses & limitations |
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The model is intended to detect addresses that occur in a sentence. |
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## Training and evaluation data |
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This model is trained on `piazzola/addressWithContext`. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- num_epochs: 3.0 |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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