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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- email generation |
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- email |
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datasets: |
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- aeslc |
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- postbot/multi_emails_kw |
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widget: |
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- text: "Thursday pay invoice need asap thanks Pierre good morning dear Harold" |
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example_title: "invoice" |
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- text: "dear elia when will space be ready need urgently regards ronald" |
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example_title: "space ready" |
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- text: "Tuesday I need review document before leaves our company need know when leave" |
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example_title: "review document" |
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- text: "dear bob will back wednesday need urgently regards elena" |
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example_title: "return wednesday" |
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- text: "dear mary thanks for your last invoice need know when payment be" |
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example_title: "last invoice" |
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- text: "dear william I out yesterday received message today will get back today" |
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example_title: "message" |
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- text: "dear joseph have all invoices ready Monday next invoice in 30 days have great weekend" |
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example_title: "next invoice" |
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- text: "dear mary I have couple questions on new contract we agreed on need know thoughts regarding contract" |
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example_title: "contract" |
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- text: "Friday will make report due soon please thanks dear john" |
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example_title: "report due soon" |
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- text: "need take photos sunday want finish thursday photo exhibition need urgent help thanks dear john" |
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example_title: "photo exhibition" |
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- text: "Tuesday need talk with you important stuff" |
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example_title: "important talk" |
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- text: "dear maria how are you doing thanks very much" |
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example_title: "thanks" |
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- text: "dear james tomorrow will prepare file for june report before leave need know when leave" |
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example_title: "file for june report" |
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parameters: |
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min_length: 16 |
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max_length: 256 |
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no_repeat_ngram_size: 2 |
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do_sample: False |
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num_beams: 8 |
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early_stopping: True |
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repetition_penalty: 2.5 |
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length_penalty: 0.9 |
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--- |
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# t5-small-kw2email-v2 |
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This model is a fine-tuned version of [postbot/t5-small-kw2email](https://huggingface.co/postbot/t5-small-kw2email) on the None dataset. |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.01 |
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- num_epochs: 4 |
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### Training results |
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### Framework versions |
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- Transformers 4.21.1 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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