FARM
Chinese-CLIP
| FARM | Chinese-CLIP | |
|---|---|---|
| 3 | 1 | |
| 1,727 | 3,701 | |
| 0.5% | 8.8% | |
| 0.0 | 7.6 | |
| 5 months ago | 6 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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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.
Chinese-CLIP
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Meet ‘Chinese CLIP,’ An Implementation of CLIP Pretrained on Large-Scale Chinese Datasets with Contrastive Learning
Chinese-CLIP is open-sourced on https://github.com/OFA-Sys/Chinese-CLIP , we are working on applying it on more downstreaming tasks requiring cross-modal alignment!
What are some alternatives?
Giveme5W1H - Extraction of the journalistic five W and one H questions (5W1H) from news articles: who did what, when, where, why, and how?
dream-creator - Quickly and easily create / train a custom DeepDream model
bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
Questgen.ai - Question generation using state-of-the-art Natural Language Processing algorithms
Queryable - Run OpenAI's CLIP model on iOS to search photos.
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
PyTorch_CIFAR10 - Pretrained TorchVision models on CIFAR10 dataset (with weights)
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
autodistill-metaclip - MetaCLIP module for use with Autodistill.
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.