Batterydata
Rank:Average Model Cost: $0.0000
Number of Runs: 91,605
Models by this creator
bde-cner-batteryonlybert-uncased-base
bde-cner-batteryonlybert-uncased-base
The bde-cner-batteryonlybert-uncased-base model is a token classification model. It is trained to recognize and classify different token types in text, such as named entities, based on the BERT architecture. This model is specifically trained to perform token classification tasks related to battery data extraction.
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44.8K
Huggingface
bde-pos-bert-cased-base
bde-pos-bert-cased-base
The bde-pos-bert-cased-base model is a token classification model that is designed to perform part-of-speech tagging. It takes in a sequence of words as input and assigns a tag to each word indicating its part of speech. This model is based on the BERT architecture and uses a cased vocabulary.
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44.6K
Huggingface
batterybert-cased-squad-v1
batterybert-cased-squad-v1
BatteryBERT-cased for QA Language model: batterybert-cased Language: EnglishDownstream-task: Extractive QATraining data: SQuAD v1 Eval data: SQuAD v1 Code: See example Infrastructure: 8x DGX A100 Hyperparameters Performance Evaluated on the SQuAD v1.0 dev set. Evaluated on the battery device dataset. Usage In Transformers Authors Shu Huang: sh2009 [at] cam.ac.uk Jacqueline Cole: jmc61 [at] cam.ac.uk Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
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696
Huggingface
batterybert-cased
batterybert-cased
BatteryBERT-uncased model Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the bert-base-cased weights. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between english and English. Model description BatteryBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the bert-base-cased weights. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. Training data The BatteryBERT model was pretrained on the full text of battery papers only, after initialized from the bert-base-cased weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at Github. Training procedure Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 28,996. The inputs of the model are then of the form: The details of the masking procedure for each sentence are the following: 15% of the tokens are masked. In 80% of the cases, the masked tokens are replaced by [MASK]. In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. In the 10% remaining cases, the masked tokens are left as is. Pretraining The model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, β1=0.9\beta_{1} = 0.9β1​=0.9 and β2=0.999\beta_{2} = 0.999β2​=0.999, a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. How to use You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: and in TensorFlow: Evaluation results Final loss: 0.9609. Authors Shu Huang: sh2009 [at] cam.ac.uk Jacqueline Cole: jmc61 [at] cam.ac.uk Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
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686
Huggingface
bde-abbrev-batteryonlybert-cased-base
bde-abbrev-batteryonlybert-cased-base
Platform did not provide a description for this model.
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438
Huggingface
bert-base-cased-squad-v1
bert-base-cased-squad-v1
bert-base-cased-squad-v1 is a BERT (Bidirectional Encoder Representations from Transformers) model pretrained on a large corpus of text data that can be used for question answering. It is trained on the Stanford Question Answering Dataset (SQuAD) and can provide answers to questions based on a given context. The model achieves this by encoding the context and question as input and producing the most probable answer span from the context. It is a cased model, meaning that it preserves the case of the input text.
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189
Huggingface
batteryonlybert-uncased-abstract
batteryonlybert-uncased-abstract
Platform did not provide a description for this model.
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70
Huggingface
batteryscibert-uncased-squad-v1
batteryscibert-uncased-squad-v1
Platform did not provide a description for this model.
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62
Huggingface
batterybert-uncased-squad-v1
batterybert-uncased-squad-v1
BatteryBERT-uncased for QA Language model: batterybert-uncased Language: EnglishDownstream-task: Extractive QATraining data: SQuAD v1 Eval data: SQuAD v1 Code: See example Infrastructure: 8x DGX A100 Hyperparameters Performance Evaluated on the SQuAD v1.0 dev set. Evaluated on the battery device dataset. Usage In Transformers Authors Shu Huang: sh2009 [at] cam.ac.uk Jacqueline Cole: jmc61 [at] cam.ac.uk Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
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56
Huggingface
batteryscibert-cased-squad-v1
batteryscibert-cased-squad-v1
BatterySciBERT-cased for QA Language model: batteryscibert-cased Language: EnglishDownstream-task: Extractive QATraining data: SQuAD v1 Eval data: SQuAD v1 Code: See example Infrastructure: 8x DGX A100 Hyperparameters Performance Evaluated on the SQuAD v1.0 dev set. Evaluated on the battery device dataset. Usage In Transformers Authors Shu Huang: sh2009 [at] cam.ac.uk Jacqueline Cole: jmc61 [at] cam.ac.uk Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
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35
Huggingface