Nickmuchi
Rank:Average Model Cost: $0.0000
Number of Runs: 76,416
Models by this creator
finbert-tone-finetuned-fintwitter-classification
finbert-tone-finetuned-fintwitter-classification
The "finbert-tone-finetuned-fintwitter-classification" model is a fine-tuned version of the "finbert-tone" model on the Twitter Financial News dataset. It is trained to determine the financial sentiment of given tweets. The model achieved an accuracy of 0.8840 and an F1 score of 0.8838 on the evaluation set. The weights of the model were adjusted to account for the unbalanced distribution of class labels. The specific training data and hyperparameters used are not provided. The model was trained using Transformers 4.25.1, Pytorch 1.13.0+cu116, Datasets 2.8.0, and Tokenizers 0.13.2.
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36.2K
Huggingface
finbert-tone-finetuned-finance-topic-classification
finbert-tone-finetuned-finance-topic-classification
The finbert-tone-finetuned-finance-topic-classification model is a text classification model that has been fine-tuned for predicting the finance-related topic of a given text. It utilizes the FinBERT model, which is pre-trained on financial news articles, and further fine-tunes it on a specific finance topic classification task. This model can be used to automatically classify text documents or snippets into finance-related topics, making it useful for tasks such as sentiment analysis, news classification, and financial document analysis.
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17.9K
Huggingface
yolos-small-finetuned-license-plate-detection
yolos-small-finetuned-license-plate-detection
The yolos-small-finetuned-license-plate-detection model is an object detection model that has been trained to specifically identify and locate license plates in images. It is based on the YOLO (You Only Look Once) small architecture, which is known for its fast and efficient performance. By using this model, developers can easily integrate license plate detection capabilities into their own applications or systems.
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11.9K
Huggingface
deberta-v3-base-finetuned-finance-text-classification
deberta-v3-base-finetuned-finance-text-classification
The deberta-v3-base-finetuned-finance-text-classification model is a finetuned version of DeBERTa-v3, a state-of-the-art transformer-based language model. This specific model has been trained on finance-related text data and can be used for various text classification tasks, such as sentiment analysis, news categorization, or financial document classification. It has been optimized to understand and analyze finance-specific language and can provide accurate predictions and insights on finance-related texts.
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8.3K
Huggingface
vit-finetuned-chest-xray-pneumonia
vit-finetuned-chest-xray-pneumonia
vit-finetuned-chest-xray-pneumonia This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the chest-xray-pneumonia dataset. It achieves the following results on the evaluation set: Loss: 0.1271 Accuracy: 0.9551 Model description More information needed Intended uses & limitations More information needed Training and evaluation data More information needed Training procedure Training hyperparameters The following hyperparameters were used during training: learning_rate: 2e-05 train_batch_size: 16 eval_batch_size: 8 seed: 42 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 10 Training results Example Images Framework versions Transformers 4.17.0 Pytorch 1.10.0+cu111 Datasets 1.18.4 Tokenizers 0.11.6
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603
Huggingface
quantized-optimum-finbert-tone
quantized-optimum-finbert-tone
Platform did not provide a description for this model.
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410
Huggingface
distilroberta-finetuned-financial-text-classification
distilroberta-finetuned-financial-text-classification
distilroberta-finetuned-financial-text-classification This model is a fine-tuned version of distilroberta-base on the sentence_50Agree financial-phrasebank + Kaggle Dataset, a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). The Kaggle dataset includes Covid-19 sentiment data and can be found here: sentiment-classification-selflabel-dataset. It achieves the following results on the evaluation set: Loss: 0.4463 F1: 0.8835 Model description Model determines the financial sentiment of given text. Given the unbalanced distribution of the class labels, the weights were adjusted to pay attention to the less sampled labels which should increase overall performance. The Covid dataset was added in order to enrich the model, given most models have not been trained on the impact of Covid-19 on earnings or markets. Training hyperparameters The following hyperparameters were used during training: learning_rate: 2e-05 train_batch_size: 64 eval_batch_size: 64 seed: 42 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 10 mixed_precision_training: Native AMP Training results Framework versions Transformers 4.15.0 Pytorch 1.10.0+cu111 Datasets 1.18.0 Tokenizers 0.10.3
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402
Huggingface
yolos-small-rego-plates-detection
yolos-small-rego-plates-detection
Platform did not provide a description for this model.
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272
Huggingface
sec-bert-finetuned-finance-classification
sec-bert-finetuned-finance-classification
Platform did not provide a description for this model.
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264
Huggingface
fb-bart-large-finetuned-trade-the-event-finance-summarizer
fb-bart-large-finetuned-trade-the-event-finance-summarizer
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:
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242
Huggingface