license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.3466 | 1ad7220be72bb75d68406da9c1a59519 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2739 | 1.0 | 4118 | 1.2801 | | 1.0001 | 2.0 | 8236 | 1.2823 | | 0.8484 | 3.0 | 12354 | 1.3466 | | fb65813cbabee334cf9dddb91dfa8e9b |
apache-2.0 | ['automatic-speech-recognition', 'ja'] | false | exp_w2v2t_ja_vp-fr_s368 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 1311c09b86fd36da7c182a8d9326b20d |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-wikitext2 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: 3.6429 | babb74fd7a7ee566d6c7b61a09f3b8fe |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7607 | 1.0 | 2334 | 3.6664 | | 3.6527 | 2.0 | 4668 | 3.6473 | | 3.6015 | 3.0 | 7002 | 3.6429 | | 418be9609bfb395b9863be606ced0451 |
other | ['vision', 'image-segmentation'] | false | SegFormer (b4-sized) model fine-tuned on ADE20k SegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https:... | d98ced9ddf2d1ddc1548175432e0e7a0 |
other | ['vision', 'image-segmentation'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation from PIL import Image import requests feature_extractor = SegformerFeatureExtractor.from_pretr... | 6a1e561ffd3db5e247f6856e58c3d283 |
apache-2.0 | ['translation'] | false | afr-spa * source group: Afrikaans * target group: Spanish * OPUS readme: [afr-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-spa/README.md) * model: transformer-align * source language(s): afr * target language(s): spa * model: transformer-align * pre-processing: normalization + Se... | 15924fd0bee18b53d3e0c15354c69923 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: afr-spa - source_languages: afr - target_languages: spa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-spa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['af', 'es'] - src_constituents: {'afr'} - tgt_const... | 9153ebf861c5b3d203427d280a4ae7b7 |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_vp-sv_s894 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | f49df6aaef3a0ff7cd82a7df8cf84465 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_100v2_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni100v2_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.4048 - Precision: 0.2783 - Recall: 0.1589 - F1: 0.2023 - Accura... | 7d5dd257895169fbd58f558ab87d0348 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 39 | 0.4802 | 0.3667 | 0.0784 | 0.1292 | 0.8125 | | No log | 2.0 |... | 3b50037cb7e5a8cef4cbd2c652116715 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the panda concept trained by zhangshengdong. This is a Stable Diffusion model fine-tuned on the panda concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of panda animal** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation pag... | 8c3bb2923b223848ed0bd4b71d12481f |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | Description This is a Stable Diffusion model fine-tuned on `animal-panda` images for the animal theme, for the Hugging Face DreamBooth Hackathon, from the HF CN Community, corporated with the HeyWhale. | 7958777fd4847bb40b18d74b3e3d8eb1 |
apache-2.0 | ['text2text-generation'] | false | Model Details - **Model Description:** 뭔가 찾아봐도 모델이나 알고리즘이 딱히 없어서 만들어본 모델입니다. <br /> BartForConditionalGeneration Fine-Tuning Model For Korean To Number <br /> BartForConditionalGeneration으로 파인튜닝한, 한글을 숫자로 변환하는 Task 입니다. <br /> - Dataset use [Korea aihub](https://aihub.or.kr/aihubdata/data/list.do?currMenu=115&topMenu... | b054566017e12fbb4b9f7767271c4184 |
apache-2.0 | ['text2text-generation'] | false | Evaluation Just using `evaluate-metric/bleu` and `evaluate-metric/rouge` in huggingface `evaluate` library <br /> [Training wanDB URL](https://wandb.ai/bart_tadev/BartForConditionalGeneration/runs/14hyusvf?workspace=user-bart_tadev) | e7f1d584c3050f81cc55a3a44589e78a |
apache-2.0 | ['text2text-generation'] | false | How to Get Started With the Model ```python from transformers.pipelines import Text2TextGenerationPipeline from transformers import AutoTokenizer, AutoModelForSeq2SeqLM texts = ["그러게 누가 여섯시까지 술을 마시래?"] tokenizer = AutoTokenizer.from_pretrained("lIlBrother/ko-TextNumbarT") model = AutoModelForSeq2SeqLM.from_pretrained(... | bce7647917e3f29cc7124274267bdfc2 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny Pashto This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the google/fleurs ps_af dataset. It achieves the following results on the evaluation set: - Loss: 0.8714 - Wer: 60.0560 | ed4f3348171d4ea675cef41ca007de52 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | da2812f8dce7aa61016a43cd4f7d263f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.9153 | 2.5 | 100 | 1.0240 | 68.9864 | | 0.6865 | 5.0 | 200 | 0.8968 | 61.7660 | | 0.5474 | 7.5 | 300 | 0.8744 | 60.555... | 805a0c12a2611f630591af37de0f43c0 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_One_500v3_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one500v3_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2659 - Precision: 0.6975 - Recall: 0.6782 - F1: 0.6877 - Accura... | 9926d359c999d5ca052473483d156ea9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 175 | 0.2990 | 0.5405 | 0.4600 | 0.4970 | 0.9007 | | No log | 2.0 |... | 6c037c7a9e7cfb558f289a23362143d6 |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_1500k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1500k 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... | 6000b75b032e6e52b7614915c5e3bcdd |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_1500k'] | 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_0-step_1500k') model = TFBertModel.from_pretrained("google/multib... | 789fc6c55c17a0df50fbf72339004401 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | - [✨Version v1✨](https://huggingface.co/bertin-project/bertin-gpt-j-6B/tree/v1): August 25th, 2022 (*[full](https://huggingface.co/bertin-project/bertin-gpt-j-6B/tree/v1) and [half-precision weights](https://huggingface.co/bertin-project/bertin-gpt-j-6B/tree/v1-half)*, at step 1M) - [Version v1beta3](https://huggingfa... | 0f46c0de5d580a1c5fc78f7c4001aa66 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Model Description BERTIN-GPT-J-6B is a Spanish finetuned version of GPT-J 6B, a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters. <figure> | Hyperpara... | 0c59038a3f829b238683431341656c6b |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Training procedure This model was finetuned for ~65 billion tokens (65,536,000,000) over 1,000,000 steps on a single TPU v3-8 VM. It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token correctly. Training took roughly 6 months. | c9ccdf168bc4d14fc28050d7008656f2 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Intended Use and Limitations BERTIN-GPT-J-6B learns an inner representation of the Spanish language that can be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating text from a prompt. | 9409b027604dd74cab08f601ca7c5ca3 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bertin-project/bertin-gpt-j-6B") model = AutoModelForCausalLM.from_pretrained("bertin-project/bertin-gpt-j-6B")... | 4f985c01643b6f491c70aed654c9b6f6 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Limitations and Biases As the original GPT-J model, the core functionality of BERTIN-GPT-J-6B is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting BERTIN-GPT-J-6B it is important to remembe... | 194a808c414ffdddbb50f93e8b3baa8a |
apache-2.0 | ['pytorch', 'causal-lm'] | false | BibTeX entry To cite this model: ```bibtex @inproceedings{BERTIN-GPT, author = {Javier De la Rosa and Andres Fernández}, editor = {Manuel Montes-y-Gómez and Julio Gonzalo and Francisco Rangel and Marco Casavantes and Miguel Ángel Álvarez-Carmona and Gemma Bel-Enguix and Hugo Jair Escalante and Laris... | fa4cd19ccaf3e5bed9d6b536c6cc4a65 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Team - Javier de la Rosa ([versae](https://huggingface.co/versae)) - Eduardo González ([edugp](https://huggingface.co/edugp)) - Paulo Villegas ([paulo](https://huggingface.co/paulo)) - Pablo González de Prado ([Pablogps](https://huggingface.co/Pablogps)) - Manu Romero ([mrm8488](https://huggingface.co/)) - María Gran... | c7693a7a1697982fdabb4dced3c8be81 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Acknowledgements This project would not have been possible without compute generously provided by the National Library of Norway and Google through the [TPU Research Cloud](https://sites.research.google/trc/), as well as the Cloud TPU team for providing early access to the [Cloud TPU VM](https://cloud.google.com/blog... | b08f83264703b4cc1ea678cd95a18aff |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Disclaimer The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems b... | 7e1cf533ff983cd471804e0e00c240b5 |
apache-2.0 | ['generated_from_trainer'] | false | bigbird-base-health-fact This model is a fine-tuned version of [google/bigbird-roberta-base](https://huggingface.co/google/bigbird-roberta-base) on the health_fact dataset. It achieves the following results on the VALIDATION set: - Overall Accuracy: 0.8228995057660626 - Macro F1: 0.6979224830442152 - False Accuracy: ... | dae540c2ee284806263e8ec5e1184c10 |
apache-2.0 | ['generated_from_trainer'] | false | Model description Here is how you can use the model: ```python import torch from transformers import pipeline claim = "A mother revealed to her child in a letter after her death that she had just one eye because she had donated the other to him." text = "In April 2005, we spotted a tearjerker on the Internet about a... | c634239820348641913404b772c91b13 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 18 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 - mixed_precision_trai... | 84ac9dab585a285917a3a621a6898867 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Micro F1 | Macro F1 | False F1 | Mixture F1 | True F1 | Unproven F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:----------:|:-------:|:-----------:| | 0.5563 | 1.0 | 1226 | 0.5020 | 0.7949 ... | ca23d7269dd35848dc914975e3418d84 |
mit | ['generated_from_trainer'] | false | deberta-base-mnli-finetuned-cola This model is a fine-tuned version of [microsoft/deberta-base-mnli](https://huggingface.co/microsoft/deberta-base-mnli) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8205 - Matthews Correlation: 0.6282 | 45aef838ee19c933e908575c1759ddb6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4713 | 1.0 | 535 | 0.5110 | 0.5797 | | 0.2678 | 2.0 | 1070 | 0.6648 | 0.5154 | | 0.1... | 510bc8cea6d1a04ef46bb4ccf4335e63 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.3822 - F1: 0.6771 | 7be561bfc05ff1d1413cd07fa53dd728 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1307 | 1.0 | 50 | 0.5745 | 0.4939 | | 0.5178 | 2.0 | 100 | 0.4389 | 0.6472 | | 0.3716 | 3.0 | 150 | 0.3822 | 0.6771 | ... | 030acddab255883e08c970320f0fd923 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad 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: 1.4386 | e1170f0470987aa49c8adfa0a2f0a7b5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.8806 | 1.0 | 106 | 1.6240 | | 1.8017 | 2.0 | 212 | 1.4700 | | 1.5078 | 3.0 | 318 | 1.4257 | | 1.3149 | 4.0 | 424 | 1.4073 ... | cda76f5b0c289aba0fc334de0bbfe2dd |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-wikitexts 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: 3.6424 | 9566e956f8e2afa1b178a24f298c54ab |
mit | [] | false | Model Description A series of CLIP [ConvNeXt-Base](https://arxiv.org/abs/2201.03545) (w/ wide embed dim) models trained on subsets LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Goals: * Explore an alternative to ViT and ResNet (w/ AttentionPooling) CLIP mod... | 9d0cd13c0dfc79787f0a0e49bb61dde3 |
mit | [] | false | Citation **BibTeX:** ```bibtex @inproceedings{schuhmann2022laionb, title={{LAION}-5B: An open large-scale dataset for training next generation image-text models}, author={Christoph Schuhmann and Romain Beaumont and Richard Vencu and Cade W Gordon and Ross Wightman and ... | f56d9baf08b35a0e7d42eafa26227967 |
mit | ['generated_from_trainer'] | false | BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-NDD-NER This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the ManpreetK/NDD_NER dataset. It achieves the following results on... | 22ea228322719e8358ff081b8071a1de |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Associated Problem Precision | Associated Problem Recall | Associated Problem F1 | Associated Problem Number | Condition Precision | Condition Recall | Condition F1 | Condition Number | Intervention Precision | Intervention Recall | Intervention F1 |... | 9414b4095b09ee93ca54c3078c1415ea |
apache-2.0 | ['generated_from_trainer'] | false | mBERT_all_ty_SQen_SQ20_1 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5305 | ce605c51b444f1ea5a91971cb129bf12 |
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: 0.2992 - eval_accuracy: 0.9085 - eval_f1: 0.9069 - eval_runtime: 2.879... | dab75545d4b0cbfc5183228cc5340114 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-0.6-0.5 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 3.5047 - Bleu: 5.2928 - Gen Len: 40.7094 | bed7c52cbbfd79b5b9c123bd98d673b3 |
mit | [] | false | jetsetdreamcastcovers on Stable Diffusion This is the `<jet>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can ... | d96c12764e9ac6f09fc52ac71ad03b8f |
apache-2.0 | ['transformers'] | false | This is a finetuned version of [RuRoBERTa-large](https://huggingface.co/sberbank-ai/ruRoberta-large) for the task of linguistic acceptability classification on the [RuCoLA](https://rucola-benchmark.com/) benchmark. The hyperparameters used for finetuning are as follows: * 5 training epochs (with early stopping based o... | 4d956ee86e49a3f941843ea2d1a84225 |
apache-2.0 | ['translation'] | false | cpp-eng * source group: Creoles and pidgins, Portuguese-based * target group: English * OPUS readme: [cpp-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cpp-eng/README.md) * model: transformer * source language(s): ind max_Latn min pap tmw_Latn zlm_Latn zsm_Latn * target language(s): e... | 69671f6804fc5a8d6c7363c63fda8d55 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.msa-eng.msa.eng | 39.6 | 0.580 | | Tatoeba-test.multi.eng | 39.7 | 0.580 | | Tatoeba-test.pap-eng.pap.eng | 49.1 | 0.579 | | eb60fca14078bd38069f108e34b877b8 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: cpp-eng - source_languages: cpp - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cpp-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['id', 'cpp', 'en'] - src_constituents: {'zsm_Latn', ... | 89eb5dc0aa388ecf7b29e2e6246b246f |
apache-2.0 | ['translation', 'generated_from_keras_callback'] | false | tf-marian-finetuned-kde4-en-to-zh_TW This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7752 - Validation Loss: 0.9022 - Epoch: 2 | fd835cc1a240e3dc314346dde634f364 |
apache-2.0 | ['translation', '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': 5e-05, 'decay_steps': 11973, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | 82ab077b04176399a01bc8f903773ec2 |
apache-2.0 | ['translation', 'generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.7752 | 0.9022 | 0 | | 0.7749 | 0.9022 | 1 | | 0.7752 | 0.9022 | 2 | | 4227b8c945c8c46bc6fa3bedee033f06 |
apache-2.0 | ['automatic-speech-recognition', 'phongdtd/VinDataVLSP', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 8 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-... | 05e908b457be092db3546d6189150dc5 |
apache-2.0 | ['generated_from_trainer'] | false | favsbot_filtersort_using_t5_summarization This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the filter_sort dataset. It achieves the following results on the evaluation set: - Loss: 2.3327 - Rouge1: 15.7351 - Rouge2: 0.0 - Rougel: 13.4803 - Rougelsum: 13.5134 - Gen Len: 12.6667 | 4dac262902e21d4295b922524c6d1658 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 5 | 3.8161 | 14.754 | 0.0 | 12.6197 | 12.6426 | 10.5 ... | cf764eafdbc37f77e6c8130aa17122a1 |
apache-2.0 | ['generated_from_keras_callback'] | false | nandysoham/20-clustered This model is a fine-tuned version of [Rocketknight1/distilbert-base-uncased-finetuned-squad](https://huggingface.co/Rocketknight1/distilbert-base-uncased-finetuned-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7888 - Train End Logits Ac... | 6472960f42592a060942216eb8231b9f |
apache-2.0 | ['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': 2e-05, 'decay_steps': 336, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_... | 3a1b3ae3355d9ad9a1b928ad7b9f5968 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | dbdda55330b214a75415d9dfd6390f0b |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-t5-summarization This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.7613 - Rouge1: 24.5755 - Rouge2: 11.8424 - Rougel: 20.3031 - Rougelsum: 23.1867 - Gen Len: 18.9999 | c3668e735a04153e0d0deebe92796efb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 - mixed_precision_training: Native AMP | c0d768b05d475ab63d5e8bcdd904a9e6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.9891 | 1.0 | 17945 | 1.7981 | 24.382 | 11.7099 | 20.1707 | 23.0021 ... | 0f1bf5a1dbc866504d1177453f1e5e8b |
apache-2.0 | ['masked-lm'] | false | AL-RoBERTa base model Pretrained model on Albanian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between tirana and Tirana. | c45636db712f528078c3066c72d9f37a |
apache-2.0 | ['masked-lm'] | false | Model description RoBERTa is a transformers model pre-trained on a large corpus of text data in a self-supervised fashion. This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs ... | cc9bb05d1759f053a08770ec15967a89 |
apache-2.0 | ['masked-lm'] | false | How to use You can use this model directly with a pipeline for masked language modeling: \ from transformers import pipeline \ unmasker = pipeline('fill-mask', model='macedonizer/al-roberta-base') \ unmasker("Tirana është \\<mask\\> i Shqipërisë.") \ [{'score': 0.9426872134208679, 'sequence': 'Tirana është kryeqyt... | 0852c53127ba2f267a5d4f1cc6d2c17c |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-snli-target-glue-sst2 This model is a fine-tuned version of [muhtasham/tiny-mlm-snli](https://huggingface.co/muhtasham/tiny-mlm-snli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4415 - Accuracy: 0.8234 | 4e9520343d0edebd464f07fcca9cdc17 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5925 | 0.24 | 500 | 0.4955 | 0.7741 | | 0.4461 | 0.48 | 1000 | 0.4719 | 0.7856 | | 0.3964 | 0.71 | 1500 | 0.4450 | 0.... | 7cebb23ebbf85203293e913e3eb3c694 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/paraphrase-distilroberta-base-v1 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. | aea18b61952f161f9ea7a7b6c47e57dc |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | be4f1fc2f083ee78b499bbf7e2428b9c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-distilroberta-base-v1') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-distilroberta-base-v1') | d89139b6f0de29d837b52edfde1fd4ac |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', '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/paraphrase-distilroberta-base-v1) | 90f70d1ece72be603fc3c7708cd0a529 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_... | 3d002b1599e0a3e49cdde341cb43eb91 |
apache-2.0 | ['generated_from_trainer', 'tex2log', 'log2tex', 'foc'] | false | T5 (small) fine-tuned on Text2Log This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an Text2Log dataset. It achieves the following results on the evaluation set: - Loss: 0.0313 | 0bb69d71a2d7814bb43c9a3f7b80200c |
apache-2.0 | ['generated_from_trainer', 'tex2log', 'log2tex', 'foc'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 | fa2a62d9ca9efe1693ecbeeace1a2488 |
apache-2.0 | ['generated_from_trainer', 'tex2log', 'log2tex', 'foc'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 0.0749 | 1.0 | 21661 | 0.0509 | | 0.0564 | 2.0 | 43322 | 0.0396 | | 0.0494 | 3.0 | 64983 | 0.0353 | | 0.0425 | 4.0 | 86644 | 0... | 694f7914f2a1fc7984af3cae4d242a69 |
apache-2.0 | ['generated_from_trainer', 'tex2log', 'log2tex', 'foc'] | false | Usage: ```py from transformers import AutoTokenizer, T5ForConditionalGeneration MODEL_CKPT = "mrm8488/t5-small-finetuned-text2log" model = T5ForConditionalGeneration.from_pretrained(MODEL_CKPT).to(device) tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT) def translate(text): inputs = tokenizer(text, padding... | 5950bd263a524e9150691bdd7693b9f6 |
mit | ['sentiment', 'bert'] | false | German Sentiment Classification with Bert This model was trained for sentiment classification of German language texts. To achieve the best results all model inputs needs to be preprocessed with the same procedure, that was applied during the training. To simplify the usage of the model, we provide a Python package ... | 721c987c0e57c5a1d5a3efc6a1d3dde9 |
mit | ['sentiment', 'bert'] | false | Using the Python package To get started install the package from [pypi](https://pypi.org/project/germansentiment/): ```bash pip install germansentiment ``` ```python from germansentiment import SentimentModel model = SentimentModel() texts = [ "Mit keinem guten Ergebniss","Das ist gar nicht mal so gut", "... | 79df927ed7294055053f3bff3d93b774 |
mit | ['sentiment', 'bert'] | false | Output class probabilities ```python from germansentiment import SentimentModel model = SentimentModel() classes, probabilities = model.predict_sentiment(["das ist super"], output_probabilities = True) print(classes, probabilities) ``` ```python ['positive'] [[['positive', 0.9761366844177246], ['negative', 0.02354... | 917621065fc163a54e8fa1d3bb9c470f |
mit | ['sentiment', 'bert'] | false | Model and Data If you are interested in code and data that was used to train this model please have a look at [this repository](https://github.com/oliverguhr/german-sentiment) and our [paper](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.202.pdf). Here is a table of the F1 scores that this model achie... | 00e79bce717219ece110e268b074f372 |
mit | ['sentiment', 'bert'] | false | Cite For feedback and questions contact me view mail or Twitter [@oliverguhr](https://twitter.com/oliverguhr). Please cite us if you found this useful: ``` @InProceedings{guhr-EtAl:2020:LREC, author = {Guhr, Oliver and Schumann, Anne-Kathrin and Bahrmann, Frank and Böhme, Hans Joachim}, title = {Tra... | e9a8fbcc9e7f1476144c6a87e928fde4 |
mit | ['generated_from_trainer'] | false | xlm-roberta-finetuned-hipe-tags-clara-1 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0799 - F1: 0.8282 - Precision: 0.8167 - Recall: 0.8400 | 575631c3b9b5a1a6898b4472997cf356 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:| | 0.2177 | 1.0 | 445 | 0.0858 | 0.7763 | 0.7568 | 0.7970 | | 0.0694 | 2.0 | 890 | 0.0810 | 0.8056 ... | 2a336d9b3c687d556cd236623801d853 |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_unispeech_s952 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | efe9300a2533a2ca19406b92ffa2a4e0 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1280 - F1: 0.8819 | aee2d19cd820a09795aaa024c5915776 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2947 | 1.0 | 715 | 0.1790 | 0.8235 | | 0.1499 | 2.0 | 1430 | 0.1365 | 0.8664 | | 0.0969 | 3.0 | 2145 | 0.1280 | 0.8819 | ... | 50ee51753b61906e7ceff67b86f1fe65 |
apache-2.0 | ['generated_from_trainer'] | false | bert-small-finetuned-finetuned-finer-longer10 This model is a fine-tuned version of [muhtasham/bert-small-finetuned-finer](https://huggingface.co/muhtasham/bert-small-finetuned-finer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3791 | 4a64216c1e9a8a9bd2a99737133c87ba |
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: 7 | 768981d344d16083f75142640463c4f7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.5687 | 1.0 | 2433 | 1.5357 | | 1.5081 | 2.0 | 4866 | 1.4759 | | 1.4813 | 3.0 | 7299 | 1.4337 | | 1.4453 | 4.0 | 9732 | 1.4084 ... | 1a4b9a36e4c5eb439698144f7b407501 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | fin2 This model is a fine-tuned version of [nlpaueb/sec-bert-base](https://huggingface.co/nlpaueb/sec-bert-base) on the fin dataset. It achieves the following results on the evaluation set: - Loss: 0.2405 - Precision: 0.9363 - Recall: 0.7610 - F1: 0.8396 - Accuracy: 0.9743 | c8f4673e3e1370feddd306c2e882750d |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 129 | 0.2186 | 0.7980 | 0.6454 | 0.7137 | 0.9653 | | No log | 2.0 |... | 680eb87bcac8bd674320c8adec4e3d38 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Polish (pl) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](http... | f7e449ebb80a378ab2b9c7e519efe92a |
apache-2.0 | ['translation'] | false | ukr-tur * source group: Ukrainian * target group: Turkish * OPUS readme: [ukr-tur](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-tur/README.md) * model: transformer-align * source language(s): ukr * target language(s): tur * model: transformer-align * pre-processing: normalization + Se... | d47692db0085799fb7817123bdde21e5 |
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