license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['minds14', 'google/xtreme_s', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| | 2.6739 | 5.41 | 200 | 2.5687 | 0.0430 | 0.1190 | | 1.4953 | 10.81 | 400 | 1.6052 | 0.5550 | 0.5692 | | 0.6177 |... | 52d72b0a41f2f2f632b4b8f743abdd69 |
apache-2.0 | ['speechbrain', 'embeddings', 'Speaker', 'Verification', 'Identification', 'pytorch', 'ECAPA', 'TDNN'] | false | Speaker Verification with ECAPA-TDNN embeddings on Zaion This repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain. The system can be used to extract speaker embeddings as well. It is trained on Voxceleb 1+ Voxceleb2 training data. For a ... | 0b5285dd449951cd6adaf502e52a297f |
apache-2.0 | ['speechbrain', 'embeddings', 'Speaker', 'Verification', 'Identification', 'pytorch', 'ECAPA', 'TDNN'] | false | Install SpeechBrain First of all, please install SpeechBrain with the following command: ``` gh repo clone aheba/speechbrain-aheba-contribs git checkout pretrain_new pip install -r requirements.txt pip install --editable . ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBra... | 60550959d728ef755e697a6cac998f68 |
apache-2.0 | ['speechbrain', 'embeddings', 'Speaker', 'Verification', 'Identification', 'pytorch', 'ECAPA', 'TDNN'] | false | Compute your speaker embeddings ```python import torch audio import torch from speechbrain.pretrained import Pretrained classifier = Pretrained.import_model(source="aheba31/test-predictor", pymodule_file="inference.py" ,class_name="EncoderClassifier") print(classifier.classify_file("/workspace/contributions/test/s... | 3516e10ef8580d4777932f5128a581d9 |
apache-2.0 | ['speechbrain', 'embeddings', 'Speaker', 'Verification', 'Identification', 'pytorch', 'ECAPA', 'TDNN'] | false | **Citing SpeechBrain** Please, cite SpeechBrain if you use it for your research or business. ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan a... | 066a3799ff2449b900a2dc7331feeeea |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for convnext_tiny.in12k_ft_in1k_384 A ConvNeXt image classification model. Pretrained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) and fine-tuned on ImageNet-1k by Ross Wightman. ImageNet-12k training done on TPUs thanks to support of the [TRC](https://sites.research.google/trc/ab... | 894901dac49af37f79bec0fa0087c1b0 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 28.6 - GMACs: 13.1 - Activations (M): 39.5 - Image size: 384 x 384 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/rwightman/pytorch-image-mode... | 231081618eb23decdceb72be56350760 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('convnext_tiny.in12k_ft_in1k_384', pretrained=True) mod... | 266d33f92bd4cea34c0f8c142a091b05 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_tiny.in12k_ft_in1k_384', pretraine... | 4ce761543eaf124fb08137829d6e0023 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_tiny.in12k_ft_in1k_384', pretrained=True... | 419c3d873444926cee323a97731b115d |
mit | ['feature-extraction', 'sentence-similarity', 'sentence-transformers'] | false | All MPNet base model (v2) for Semantic Search This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | d8cafa71f5dbb2f5427f71d1bdbb5ba4 |
mit | ['feature-extraction', 'sentence-similarity', 'sentence-transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-mpnet-base-v2) ------ | 8cef80ae367593a19d776fdd2e2ff7c6 |
mit | ['feature-extraction', 'sentence-similarity', 'sentence-transformers'] | false | Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a 1B sentence pairs dataset. We use a co... | b468e7171d6484821f99524180f58ccb |
mit | ['feature-extraction', 'sentence-similarity', 'sentence-transformers'] | false | Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 384 word p... | a82a2a951c259e5f72a0c3ef836beb2f |
mit | ['feature-extraction', 'sentence-similarity', 'sentence-transformers'] | false | Pre-training We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure. | 82b5f4652c51bb97f786c86b12c626ab |
mit | ['feature-extraction', 'sentence-similarity', 'sentence-transformers'] | false | Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. | b47b4fc8f0e65b67ae44a975c884e3a4 |
mit | ['feature-extraction', 'sentence-similarity', 'sentence-transformers'] | false | Hyper parameters We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. | ac2749617d060428860d2cd76ccb43bc |
mit | ['feature-extraction', 'sentence-similarity', 'sentence-transformers'] | false | Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. | Dataset ... | 98de1b82d59e0a201fd2eb65c614bd85 |
mit | ['generated_from_trainer'] | false | deberta-finetuned-ner-connll-late-stop This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.5259 - Precision: 0.8302 - Recall: 0.8471 - F1: 0.8386 - Accuracy: 0.9229 | ad0460f6981247ce14beeb8f3c158102 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3408 | 1.0 | 1875 | 0.3639 | 0.7462 | 0.7887 | 0.7669 | 0.8966 | | 0.2435 | 2.0 ... | a97f58ac08857270f0c2a7c51991e3c1 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-idrak-paperspace1 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3623 - Wer: 0.3471 | 31542c4385a94a2a286fc6a096a808ad |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1034 | 0.87 | 500 | 0.3623 | 0.3471 | | 58dc7c22097bbe6ca9012740d77aa94d |
mit | ['generated_from_trainer'] | false | robbert-twitter-sentiment-tokenized This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset. It achieves the following results on the evaluation set: - Loss: 0.5473 - Accuracy: 0.814 - F1: 0.8133 - Precision: 0.8131 ... | 2c30a99719dc68eae8204547499b27ec |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.6895 | 1.0 | 282 | 0.6307 | 0.7433 | 0.7442 | 0.7500 | 0.7433 | | 0.4948 | 2.0 |... | dabc9b27ad802ed4f98925a3fd6fe7fd |
apache-2.0 | ['text reranking'] | false | BibTeX entry and citation info ```bibtex @inproceedings{gao2021lce, title={Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline}, author={Luyu Gao and Zhuyun Dai and Jamie Callan}, year={2021}, booktitle={The 43rd European Conference On Info... | 56ef2eac9600d1e573b0015f0e414fcb |
creativeml-openrail-m | ['text-to-image'] | false | cybertruck01 Dreambooth model trained by cormacncheese with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-s... | a343bb795f59dfdd682f964071944c37 |
apache-2.0 | ['generated_from_trainer'] | false | TUF_BERT_5E This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3251 - Accuracy: 0.9467 | aa1103b1a4f10f33d317d11ccca42908 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4078 | 0.1 | 50 | 0.2430 | 0.92 | | 0.2488 | 0.2 | 100 | 0.1465 | 0.94 | | 0.1966 | 0.3 | 150 | 0.1284 | 0.... | 42522712b32777145a6f54587c09a370 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_wnli_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6907 - Accuracy: 0.5634 | b42c46a79d375df3691c6dd38b39efda |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6938 | 1.0 | 5 | 0.6911 | 0.5634 | | 0.6933 | 2.0 | 10 | 0.6917 | 0.5634 | | 0.6931 | 3.0 | 15 | 0.6920 | 0.... | faabb24200c86c5ce5bc4168ed6bd92b |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | openai/whisper-small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9750 - Wer: 21.3693 | 957eeb908100d8a61b92aa07c6d59c8d |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precis... | 8d5de3635ec778662c566aa2f9d2e511 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.3559 | 0.1 | 1000 | 0.9147 | 29.3252 | | 0.3154 | 0.2 | 2000 | 1.1353 | 26.5718 | | 0.359 | 0.3 | 3000 | 0.9208 | 2... | 888e5e315caab060a2f553d84b39dd11 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | vit-base-beans This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 2.1333 - Accuracy: 0.6224 | b1906e6095519085118879b0093514f8 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 100 | 1cb432222f2cb404b1824d6dca43d4fe |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.1678 | 0.02 | 100 | 2.1333 | 0.6224 | | 762db3736e68590d7b67b988915d8357 |
apache-2.0 | ['generated_from_trainer', 'pt', 'robust-speech-event'] | false | wav2vec2-large-xlsr-coraa-portuguese-cv7 This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.1777 - Wer: 0.1339 | 4bc1fc5859960fc592b41bfde6ee13ae |
apache-2.0 | ['generated_from_trainer', 'pt', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4779 | 0.13 | 100 | 0.2620 | 0.2020 | | 0.4505 | 0.26 | 200 | 0.2339 | 0.1998 | | 0.4285 | 0.39 | 300 | 0.2507 | 0.2109 | |... | 022fe27205f8f85c27a16207a797616d |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | companioncube Dreambooth model trained by Wusul with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-d... | d619e6e966fe1dbd675fdc3b4a0b1c91 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-utility-6-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3728 - Accuracy: 0.3956 | 0a539f693b3f9ac9d860a91b5842f12f |
apache-2.0 | ['translation'] | false | cat-deu * source group: Catalan * target group: German * OPUS readme: [cat-deu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cat-deu/README.md) * model: transformer-align * source language(s): cat * target language(s): deu * model: transformer-align * pre-processing: normalization + Sente... | b0614aa9599b6f9cae5e7bd3c306e44a |
apache-2.0 | ['translation'] | false | System Info: - hf_name: cat-deu - source_languages: cat - target_languages: deu - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cat-deu/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ca', 'de'] - src_constituents: {'cat'} - tgt_const... | 65867da7f9a2b11a7104362386d21898 |
apache-2.0 | ['generated_from_trainer'] | false | Article_50v6_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v6_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.5618 - Precision: 0.0939 - Recall: 0.0192 - F1: 0.0318 - Accuracy: 0.78... | 326e492354ad6e13e4cb99ad61df8d42 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 23 | 0.6891 | 0.1429 | 0.0002 | 0.0005 | 0.7772 | | No log | 2.0 |... | 131ff85e0dc7f2afb88ac3c9cd3ca6f6 |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-tamilmixsentiment This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.4572 | 4e9a4ae5d76e447f7435170465ffca6c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.6438 | 1.0 | 907 | 4.8026 | | 4.774 | 2.0 | 1814 | 4.5953 | | 4.5745 | 3.0 | 2721 | 4.5070 | | 4.4677 | 4.0 | 3628 | 4.4688 ... | a338365b99c2c1bdd505259cf759241b |
apache-2.0 | ['generated_from_trainer'] | false | bart-paraphrase-pubmed-1.1 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4236 - Rouge2 Precision: 0.8482 - Rouge2 Recall: 0.673 - Rouge2 Fmeasure: 0.7347 | 3dbbbcac76a6b8d1f64607d8940cdad6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.6534 | 1.0 | 663 | 0.4641 | 0.8448 | 0.6691 | 0.7313 ... | 7a6e2cae9a5b6b167c1c34a81ee94875 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1538 - eval_accuracy: 0.934 - eval_f1: 0.9344 - eval_runtime: 2.0513... | 85bf7133d1180746dd047b82df5ba4ce |
mit | ['luke', 'question-answering', 'squad', 'pytorch', 'transformers', 'question answering'] | false | このモデルはluke-japanese-base-liteをファインチューニングして、Question-Answeringに用いれるようにしたものです。 このモデルはluke-japanese-base-liteをJSQuAD ( https://github.com/yahoojapan/JGLUE )を用いてファインチューニングしたものです。 Question-Answeringタスク（SQuAD）に用いることができます。 | 26845a911c1f23283a41f8b7ce58e64d |
mit | ['luke', 'question-answering', 'squad', 'pytorch', 'transformers', 'question answering'] | false | This model is fine-tuned model for Question-Answering which is based on luke-japanese-base-lite This model is fine-tuned by using JSQuAD dataset. You could use this model for Question-Answering tasks. | 3c2e3b30e007ee638ddf0eaaff21076b |
mit | ['luke', 'question-answering', 'squad', 'pytorch', 'transformers', 'question answering'] | false | How to use 使い方 以下のコードを実行することで、Question-Answeringタスクを解かせることができます。 please execute this code. ```python import torch from transformers import MLukeTokenizer, AutoModelForQuestionAnswering tokenizer = MLukeTokenizer.from_pretrained('Mizuiro-sakura/luke-japanese-base-lite-jsquad') model=AutoModelForQuestionAnswering.from_... | ba140ed0b8cc30c0f042ae244f22c1b4 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_1000k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1000k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different... | 17ca3e9a0597c41fd7821433dd206a51 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_1000k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_1000k') model = TFBertModel.from_pretrained("google/multib... | f86a9b405010ac17de22ce58d555dff1 |
gpl-3.0 | ['object-detection', 'yolo', 'autogenerated-modelcard'] | false | Model Description <!-- Provide a longer summary of what this model is. --> YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance. - **Developed by:** [More Information Needed] - **Shared by [Optional]:** [@nateraw](http... | a26e157931a87b11b0c86fc8d623732e |
creativeml-openrail-m | ['text-to-image'] | false | Visual Kei Part Two Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingf... | 4b5b6face7ec1c7810af1a0dc1c7542d |
cc-by-4.0 | ['answer extraction'] | false | Model Card of `lmqg/bart-base-squad-ae` This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for answer extraction on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation... | 4cb164172cfa187aa9c3942c34b49728 |
cc-by-4.0 | ['answer extraction'] | false | model prediction answers = model.generate_a("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/bart-base-squad-ae") output = pipe("<hl> Beyonce further expanded her act... | 93eb280783275b97307b139e8179a2b8 |
cc-by-4.0 | ['answer extraction'] | false | Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/bart-base-squad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-------... | c5d98bc74e7375d0256c11959725d472 |
cc-by-4.0 | ['answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: None - model: facebook/bart-base - max_length: 512 - max_length_output: 32 - epoch: 4 - ... | 8d8310259c52995b8edf48151633cbf0 |
apache-2.0 | ['generated_from_trainer'] | false | bert-small-finetuned-finetuned-parsed-longer100 This model is a fine-tuned version of [muhtasham/bert-small-finetuned-finetuned-parsed-longer50](https://huggingface.co/muhtasham/bert-small-finetuned-finetuned-parsed-longer50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.63... | d94bc184da407b880f87453e9350bc6f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 | f09985a6ca2ea73213e66f317d60c477 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 4 | 2.9464 | | No log | 2.0 | 8 | 2.6667 | | No log | 3.0 | 12 | 3.2662 | | No log | 4.0 | 16 | 2.6736 ... | 180aab2d0d9914e77053bbb240fb3a33 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | BARTkrame-abstract-mT5 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2557 - Rouge1: 0.2223 - Rouge2: 0.0735 - Rougel: 0.1826 - Rougelsum: 0.1849 | 975096f4b03bd94a0fad1e89f243eb55 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 9d9931a170563e4f9d85f9d6fae27f10 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 4.9563 | 1.0 | 1250 | 2.3674 | 0.2206 | 0.0755 | 0.1853 | 0.1869 | | 3.1856 | 2.0 | 2500 ... | 4c006acbb2303f85aeb2f54a2959eac9 |
mit | [] | false | dapBERT DapBERT is a BERT-like model trained based on the domain adaptive pretraining method ([Gururangan et al.](https://aclanthology.org/2020.acl-main.740/)) for the patent domain. Bert-base-uncased is used as base for the training. The training dataset used consists of a corpus of 10,000,000 patent abstracts that ... | bfa77a2a0f62686071da540808ee58c1 |
apache-2.0 | ['generated_from_trainer'] | false | semeval23-t3-st1-en-babe-distilbert-base-uncased-finetuned-sst-2-english This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss:... | 22e2b6910db08a201d0529360ec0ce29 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 22 | 3.4120 | 0.3965 | | No log | 2.0 | 44 | 3.4436 | 0.3834 | | No log | 3.0 | 66 | 3.5298 | 0.4120 | |... | b321f1c49787b5926d39ba1fc2219729 |
mit | ['generated_from_keras_callback'] | false | Ashraf-kasem/custom_gpt2_frames_text_original_tokenizer This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.1074 - Validation Loss: 1.6432 - Epoch: 29 | c2b94d18ba25da801c5068ebd44bfa66 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 240780, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'be... | 22aac5b64bdb13a407361ae2b56e53df |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.3075 | 3.4095 | 0 | | 3.1973 | 2.8234 | 1 | | 2.7420 | 2.5057 | 2 | | 2.4541 | 2.3022 | 3 | | 2.2507 | 2.1648 | 4 | | 2.0962 |... | 631088d70209d9f370828cff408b4c8b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-infovqa This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8872 | 9a521b216c3a209a788d1dba8072a237 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 250500 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 5688eb66c317aa7a124ac4f87325136f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.02 | 100 | 4.7706 | | No log | 0.05 | 200 | 4.4399 | | No log | 0.07 | 300 | 3.8175 | | No log | 0.09 | 400 | 3.8306 ... | da0097ef635b0103156f7450de35ddcd |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | wav2vec2-common_voice-tamil This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TA dataset. It achieves the following results on the evaluation set: - Loss: 1.1172 - Wer: 1.0070 | 6387c2fafb110bc63fe946af72dfd97f |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 051e5654c0fbd46f9c51c535397b38ed |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.84 | 100 | 4.0148 | 1.0 | | No log | 1.69 | 200 | 3.1738 | 1.0 | | No log | 2.54 | 300 | 2.5980 | 1.0236 | ... | d02ca4586a6db5510ac521b3742c6b81 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8559 - Bleu: 52.8365 | 6e9fe3dd6c125bd5b30c7f534e9fe40c |
apache-2.0 | ['generated_from_keras_callback'] | false | susnato/my_food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0074 - Validation Loss: 0.2560 - Train Accuracy: 0.945 - Epoch:... | 5889c3438e80a06108614126802290c4 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | bfe1dae2028695d4157e8b0b895d38c7 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.0180 | 0.2310 | 0.946 | 0 | | 0.0126 | 0.2385 | 0.946 | 1 | | 0.0104 | 0.2445 | 0.944 | 2 | | 0.0088 ... | ceb709b09389fbe4244c6f44cb72f8a4 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_700k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 700k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different ... | d7c1760937c5070bc38d2769857b5d0e |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_700k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_700k') model = TFBertModel.from_pretrained("google/multibe... | b1e48647e2783d6ea0be213ee9379cae |
mit | [] | false | Oleg KOG on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook | 31f0482fdd34fead7e973b01ecc006f8 |
mit | ['generated_from_trainer'] | false | epic_euler This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-3... | 49c0ccfa670bd7fbb6427c1ae4b0b2d5 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.0}, ... | 49b40bddea32ce2f5c5e11790280adc2 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5206 - Wer: 0.3388 | 60c6823ab9b9b7a220a6e3553536beac |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5597 | 1.0 | 500 | 2.3415 | 0.9991 | | 0.9759 | 2.01 | 1000 | 0.5556 | 0.5382 | | 0.4587 | 3.01 | 1500 | 0.7690 | 0.478... | 1a4542dc3ef538d960bda6284705e5b1 |
apache-2.0 | ['generated_from_trainer'] | false | model-2-bart-reverse-raw This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1556 - Rouge1: 63.5215 - Rouge2: 58.8297 - Rougel: 60.5701 - Rougelsum: 63.2683 - Gen L... | 5386f08747bd4cc5667df308b3838cb1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.1276 | 1.0 | 12767 | 0.1556 | 63.5215 | 58.8297 | 60.5701 | 63.2683 |... | a5f9833d076e27ed3d7d7745d644c05f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_mnli_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.9264 - Accuracy: 0.6353 | 0fd7fbe4390be3892f3e491d3a743220 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 0.799 | 1.0 | 31440 | 0.9061 | 0.6341 | | 0.5094 | 2.0 | 62880 | 1.0978 | 0.6270 | | 0.3276 | 3.0 | 94320 | 1.3038 ... | f4a4efd9d7b4aa847f9a03bf061c9fa6 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/csmsc_tts_train_conformer_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4031955/ This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | e3a081537f14ea6c1e66929ce7a4e1d6 |
apache-2.0 | ['generated_from_keras_callback'] | false | gogzy/t5-base-finetuned_renre_2021_item1 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 6.0647 - Validation Loss: 4.9004 - Train Rouge1: 14.8649 - Train Rouge2: 8.2192 - Train Rougel: 12.1622... | c6c98224e6276cc167b24e45e177c49b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 10.3805 | 9.9375 | 14.8649 | 8.2192 ... | 6d49f8c04167bc2c08941458e63a4c5f |
apache-2.0 | ['generated_from_trainer'] | false | finetuning11 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan - Wer: 1.0 | f0c00eb705bed812fb8d08d895e1d62b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00024 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | f07cdf3b94f16d660f2bac58b378587f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 0.0 | 0.31 | 500 | nan | 1.0 | | 0.0 | 0.61 | 1000 | nan | 1.0 | | 0.0 | 0.92 | 1500 | nan | 1.0 | | 0.0 ... | b840fba6ba8f17c76819f1a060d14b46 |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_vp-it_s265 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 556147ce6ee040bfef9469ad3c4ce2d9 |
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