Ibm
Rank:Average Model Cost: $0.0000
Number of Runs: 8,498
Models by this creator
re2g-reranker-nq
re2g-reranker-nq
Model Card for NQ Reranker in Re2G Model Details Training, Evaluation and Inference The code for training, evaluation and inference is in our github in the re2g branch. Usage The best way to use the model is by adapting the reranker_apply.py Citation Model Description The model creators note in the associated paper: Developed by: IBM Shared by [Optional]: IBM Model type: Query/Passage Reranker Language(s) (NLP): English License: Apache 2.0 Parent Model: BERT-base trained on MSMARCO Resources for more information: GitHub Repo Associated Paper Uses Direct Use This model can be used for the task of reranking passage results for a question. Citation BibTeX:
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5.0K
Huggingface
knowgl-large
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1.3K
Huggingface
mpt-7b-instruct2
mpt-7b-instruct2
MPT-7B-Instruct2 MPT-7B-Instruct2 is a retrained version of the orignal MPT-7B-Instruct model available under the Apache 2.0 License. MPT-7B-Instruct is a short-form instruction following decoder-only model. See model cards for MPT-7B and MPT-7B-Instruct for more information.
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603
Huggingface
ColD-Fusion
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206
Huggingface
gpt2-medium-multiexit
gpt2-medium-multiexit
Pre-trained language model with identical parameters to gpt2-medium, but with additional language modeling heads ("exits") connected to different layers of the model. These 12 additional heads (in layers 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24) were trained on the English portion of CC-100 while keeping the original pre-trained model parameters frozen. The model can be used for the Autocontrastive Decoding text generation approach described in Gera et al. 2023, for early-exiting approaches, or for other algorithms that consider the next-token predictions of different model layers. Harnessing the additional language modeling heads requires loading the model using the auto-contrastive-generation library (pip install autocontrastive-gen). In a nutshell, the user creates a MultiExitConfiguration that determines model behavior at training and inference, and then loads the model using the dedicated AutoMultiExitModel class. After that, the model can be used with the transformers API like any other model. See the GitHub for detailed usage instructions. For example, the code below initializes the model to use Autocontrastive Decoding, and then performs text generation in this chosen setting: Ariel Gera, Roni Friedman, Ofir Arviv, Chulaka Gunasekara, Benjamin Sznajder, Noam Slonim and Eyal Shnarch. The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers. ACL 2023.
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60
Huggingface
roberta-large-vira-intents
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47
Huggingface
ia-multilingual-transliterated-roberta
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30
Huggingface