FARM
happy-transformer
| FARM | happy-transformer | |
|---|---|---|
| 3 | 1 | |
| 1,723 | 500 | |
| 0.3% | - | |
| 0.0 | 9.0 | |
| 4 months ago | about 1 month ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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FARM
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Can someone please explain to me the differences between train, dev and test datasets?
I'm also trying to solve this task in a python notebook (.ipynb) using the FARM framework https://farm.deepset.ai/ and BERT model of huggingface https://huggingface.co/bert-base-uncased
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Fine-Tuning Transformers for NLP
For anyone looking to fine-train transformers with less work, there is the FARM project (https://github.com/deepset-ai/FARM) which has some more or less ready-to-go configurations (classification, question answering, NER, and a couple of others). It's really almost "plug in a csv and run".
By the way, a pet peeve is sentiment detection. It's a useful method, but please be aware that it does not measure "sentiment" in a way that one would normally think, and that what it measure varies strongly across methods (https://www.tandfonline.com/doi/abs/10.1080/19312458.2020.18...).
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Has anyone deployed a BERT like model across multiple tasks (Multi-class, NER, outlier detection)? Seeking advice.
You can use https://github.com/deepset-ai/FARM or https://github.com/nyu-mll/jiant for multitask learning. The second is more general.
happy-transformer
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GPT-Neo-125M-AID (Mia) oversight + retrained
This appears to be an actual issue with Happy Transformer judging by a GitHub issue I've found of the same problem.
What are some alternatives?
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