license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['generated_from_trainer'] | 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: 3.0 | cfce82305bcce3020997cf391d52a74c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7465 | 1.0 | 142 | 0.1972 | 0.9286 | | 0.1416 | 2.0 | 284 | 0.1080 | 0.9859 | | 0.0541 | 3.0 | 426 | 0.0936 | 0.9859 | ... | 381c3bc23ccccf3650b81b831d5371de |
mit | [] | false | James Web space Telescope on Stable Diffusion This is the `<James-Web-Telescope>` 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... | d9d0281aa71c052e6b706dbbce68bd9f |
mit | [] | false | BART-SLED (SLiding-Encoder and Decoder, base-sized model) SLED models use pretrained, short-range encoder-decoder models, and apply them over long-text inputs by splitting the input into multiple overlapping chunks, encoding each independently and perform fusion-in-decoder | 9b9382c006122860fcaff6ca47bdc1f8 |
mit | [] | false | Model description This SLED model is based on the BART model, which is described in its [model card](https://huggingface.co/facebook/bart-base). BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, ... | 03483dcd9bfbd088a51bab0ee70ae4be |
mit | [] | false | How to use To use the model, you first need to install `py-sled` in your environment (or clone the code from the [official repository](https://github.com/Mivg/SLED/blob/main/README.md)) ``` pip install py-sled ``` For more installation instructions, see [here](https://github.com/Mivg/SLED | 88692432900396ea91c3d7d00f45a3b9 |
mit | [] | false | Installation). Once installed, SLED is fully compatible with HuggingFace's AutoClasses (AutoTokenizer, AutoConfig, AutoModel and AutoModelForCausalLM) and can be loaded using the from_pretrained methods ```python import sled | e17d1469a9a5a44e8c832a6948d4fba4 |
mit | [] | false | *** required so that SledModels will be registered for the AutoClasses *** model = AutoModel.from_pretrained('tau/bart-base-sled') ``` Here is how to use this model in PyTorch: ```python from sled import SledTokenizer, SledModel tokenizer = SledTokenizer.from_pretrained('tau/bart-base-sled') model = SledModel.from_p... | 6acf47219d9b085b48c5ccf4dac9428b |
mit | [] | false | *** required so that SledModels will be registered for the AutoClasses *** tokenizer = AutoTokenizer.from_pretrained('tau/bart-base-sled') model = AutoModel.from_pretrained('tau/bart-base-sled') document_input_ids = tokenizer("Dogs are great for you.", return_tensors="pt").input_ids prefix_input_ids = tokenizer("Are d... | 82a975c4ec0baf9499da38953195235b |
mit | [] | false | BibTeX entry and citation info Please cite both the SLED [paper](https://arxiv.org/abs/2208.00748.pdf) and the BART [paper](https://arxiv.org/abs/1910.13461) by Lewis et al as well as ContractNLI by Koreeda and Manning ```bibtex @inproceedings{Ivgi2022EfficientLU, title={Efficient Long-Text Understanding with Shor... | ac863129722eca319c4c3425212c097a |
mit | ['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: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | 9e2afe86b6a35f6ba64d484b351163d2 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.5576 | 1.0 | 2249 | 6.4681 | | 6.1905 | 2.0 | 4498 | 6.1976 | | 6.0005 | 3.0 | 6747 | 6.1095 | | 41e07ad831a8b2fb0bf7237251a29ba9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-mnli 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: 0.8311 - Accuracy: 0.6574 | b6e1234249adf7bca2a9a243af476688 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8687 | 1.0 | 2636 | 0.8341 | 0.6495 | | 0.7788 | 2.0 | 5272 | 0.8311 | 0.6574 | | 56f8015676cb299c9f77e6898b091dab |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_unispeech_s493 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 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | ed44f89019982b8dd39036e07ef9162c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - 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: 1000 - num_epochs: 1 - mixed_precision_tr... | e0096dbce357a7c8d8aff60b286c79e2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-06 - 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: 3 | d55813e58c1be8d8da37d8b92b5d0eb4 |
cc-by-4.0 | ['answer extraction'] | false | Model Card of `lmqg/mt5-base-itquad-ae` This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for answer extraction on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ... | 4415bb7ee799876fb4e82dd8fa967914 |
cc-by-4.0 | ['answer extraction'] | false | Overview - **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base) - **Language:** it - **Training data:** [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/l... | 07d8f54c885f48900614c1c20770edc1 |
cc-by-4.0 | ['answer extraction'] | false | model prediction answers = model.generate_a("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-base-itquad-ae") output = pipe("<hl> Il 6 ottobre 1973 , la... | 047afcff5caf42da54f83e41f7860207 |
cc-by-4.0 | ['answer extraction'] | false | Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-itquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_itquad.default.json) | | Score | Type | Dataset | |:----... | 0f91da40a59eb55b39b7b2e2ee5ebdf7 |
cc-by-4.0 | ['answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_itquad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: None - model: google/mt5-base - max_length: 512 - max_length_output: 32 - epoch: 16 - b... | 477a2e506e8225ac73b36ac1b07453a6 |
apache-2.0 | ['generated_from_trainer'] | false | t5-sentiment-hub 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: - Loss: 0.0608 - Rouge1: 97.0464 - Rouge2: 0.0 - Rougel: 97.0464 - Rougelsum: 97.0464 - Gen Len: 2.0 | 67e699c4e289b4c5827248fc6effec39 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-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: 5 - mixed_precision_training: Native AMP | fd728cb0e64be9db681b9bc9b119d34b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 0.2061 | 0.84 | 250 | 0.1437 | 90.7173 | 0.0 | 90.7173 | 90.7173 | 2.0 ... | e267e7ea6c6bc8c9742cf3460ca9cc26 |
cc-by-4.0 | ['multilingual', 'bert'] | false | ALBERTI ALBERTI is a set of two BERT-based multilingual model for poetry. One for verses and another one for stanzas. This model has been further trained with the PULPO corpus for verses using [Flax](https://github.com/google/flax), including training scripts. This is part of the [Flax/Jax Community Week](https://di... | e407e7c65cb310bb8d908c691af43b9c |
cc-by-4.0 | ['multilingual', 'bert'] | false | PULPO PULPO, the Prodigious Unannotated Literary Poetry Corpus, is a set of multilingual corpora of verses and stanzas with over 95M words. The following corpora has been downloaded using the [Averell](https://github.com/linhd-postdata/averell/) tool, developed by the [POSTDATA](https://postdata.linhd.uned.es/) team... | c8c95286323d25f3b13dee0d013b8e9e |
cc-by-4.0 | ['multilingual', 'bert'] | false | Spanish - [Disco v3](https://github.com/pruizf/disco) - [Corpus of Spanish Golden-Age Sonnets](https://github.com/bncolorado/CorpusSonetosSigloDeOro) - [Corpus general de poesía lírica castellana del Siglo de Oro](https://github.com/bncolorado/CorpusGeneralPoesiaLiricaCastellanaDelSigloDeOro) - [Gongocorpus](https://g... | a37c57836df99248eed940e1609e44ba |
cc-by-4.0 | ['multilingual', 'bert'] | false | German - [TextGrid Poetry Corpus](https://github.com/linhd-postdata/textgrid-poetry) - [source](https://textgrid.de/en/digitale-bibliothek) - [German Rhyme Corpus](https://github.com/tnhaider/german-rhyme-corpus) | 7d3b37d033342b31e566bb6ff96a367f |
cc-by-4.0 | ['multilingual', 'bert'] | false | Team members - Álvaro Pérez ([alvp](https://huggingface.co/alvp)) - Javier de la Rosa ([versae](https://huggingface.co/versae)) - Aitor Díaz ([aitordiaz](https://huggingface.co/aitordiaz)) - Elena González-Blanco - Salvador Ros ([salva](https://huggingface.co/salva)) | 4debc74dbff22190601524631c999c98 |
cc-by-4.0 | ['multilingual', 'bert'] | false | summary-timeline-calendar-6) - [Community Week README](https://github.com/huggingface/transformers/blob/master/examples/research_projects/jax-projects/README.md) - [Community Week thread](https://discuss.huggingface.co/t/bertin-pretrain-roberta-large-from-scratch-in-spanish/7125) - [Community Week channel](https://disc... | b01ca960457bf31ad2fa386928c084ed |
cc-by-4.0 | ['multilingual', 'bert'] | false | Acknowledgments This project would not have been possible without the infrastructure and resources provided by HuggingFace and Google Cloud. Moreover, we want to thank POSTDATA Project (ERC-StG-679528) and the Computational Literary Studies Infrastructure (CLS INFRA No. 101004984) of the European Union's Horizon 2020... | 1d3c3f2b996118ae06a46595b5e4985b |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de 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: 0.1446 - F1: 0.8609 | 9dc93b95f388c311632a655388021e85 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 3 | 81da3b52f3658dc6fbf32cbe6e513b23 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2623 | 1.0 | 787 | 0.1756 | 0.8132 | | 0.1321 | 2.0 | 1574 | 0.1497 | 0.8458 | | 0.0856 | 3.0 | 2361 | 0.1446 | 0.8609 | ... | 6e7e61c59a23844970b3334bd547797c |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2_common_voice_accents_indian_only_rerun This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.2807 | 3ea4e17072681b9ebbd007e64dcca77c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 48 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 384 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon... | b1b82edb633739f5ddeed175682ed6e0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.6205 | 25.0 | 400 | 1.4584 | | 0.3427 | 50.0 | 800 | 1.8377 | | 0.1213 | 75.0 | 1200 | 1.6086 | | 0.0643 | 100.0 | 1600 | 1.5136 ... | 2b2d8139947f2f2a1dc1bafc134520df |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased.CEBaB_confounding.food_service_positive.sa.5-class.seed_44 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.7505 - Accuracy: 0.6892 - Macro-f1: 0.... | 33a830beea621a6cddde2f73e2733e2a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 44 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 | 79a1f5c3bcc9440db3c3a6d019538ae6 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab0 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: 0.8768 - Wer: 0.6089 | acc6142e40ff5b91d76c98e9b25e089b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_t... | ac8f99a0704b6f3bfe5824f10e817236 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.1121 | 13.89 | 500 | 2.9931 | 1.0 | | 1.1475 | 27.78 | 1000 | 0.8768 | 0.6089 | | 4bab3da90898627991bf0152b2c724b7 |
apache-2.0 | [] | false | bert-base-pt-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the sam... | e4c195f5deaa3e78ea00e743ab182ba3 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-pt-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-pt-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https:/... | 22f4e0652707fda4f6d1a702d47c81ff |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5254 - Matthews Correlation: 0.5475 | 3f9c722aac35cc6b2e60bbaaeae26689 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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: 5 | 139091356b08320e034ed30d32ad22e7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5221 | 1.0 | 535 | 0.5360 | 0.4307 | | 0.3491 | 2.0 | 1070 | 0.5128 | 0.4972 | | 0.2... | c5d24e3723ee131bb7c57a9bae92fe8f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4721 | 51dd6869b502ede242682c86d7d497ef |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7086 | 1.0 | 157 | 2.4897 | | 2.5796 | 2.0 | 314 | 2.4230 | | 2.5269 | 3.0 | 471 | 2.4354 | | 7cf9c7a23859dcb32b6893233fb33873 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | marblesh This is a fine-tuned Stable Diffusion model (based on v1.5) trained on screenshots from marble statues. This model is a merge from 2 checkpoints trained on different marble statues. Use the token "**marblesh**" in your prompt for person and animals. If you have veichles or other object in your prompt use the ... | 8af2123134265bba34a4884e484a69a0 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the output... | 421682cd8e1929f30c26183136b03aac |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_German-HDT | Feature | Description | | --- | --- | | **Name** | `de_udv25_germanhdt_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_t... | c490533fde18afbd9c2b55e3e795bbc2 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (62832 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `$(`, `$,`, `$.`, `ADJA`, `ADJD`, `ADV`, `APPO`, `APPR`, `APPRART`, `APZR`, `ART`, `CARD`,... | 0ef6d84ff83e2e13c1c776a24861f875 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 100.00 | | `TOKEN_P` | 100.00 | | `TOKEN_R` | 100.00 | | `TOKEN_ACC` | 100.00 | | `SENTS_F` | 99.75 | | `SENTS_P` | 99.74 | | `SENTS_R` | 99.76 | | `TAG_ACC` | 97.84 | | `POS_ACC` | 97.82 | | `MORPH_ACC` | 78.11 | | `DEP_UAS` | 97.28 | | `DEP_LAS` | 95.88 | | `LEM... | 4092c34b2df771ed2734f7b06ba0286a |
apache-2.0 | ['image-classification', 'pytorch', 'onnx'] | false | Latest stable release You can install the last stable release of the package using [pypi](https://pypi.org/project/pylocron/) as follows: ```shell pip install pylocron ``` or using [conda](https://anaconda.org/frgfm/pylocron): ```shell conda install -c frgfm pylocron ``` | bf5419a7cc04b5328163345a6fa4e548 |
apache-2.0 | ['image-classification', 'pytorch', 'onnx'] | false | Developer mode Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*: ```shell git clone https://github.com/frgfm/Holocron.git pi... | e96926e8958878fbc9b79f4f83cdd39e |
apache-2.0 | ['image-classification', 'pytorch', 'onnx'] | false | Usage instructions ```python from PIL import Image from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize from torchvision.transforms.functional import InterpolationMode from holocron.models import model_from_hf_hub model = model_from_hf_hub("frgfm/resnet18").eval() img = Imag... | fb5dee108ac6c42f923d66f65e824b3b |
apache-2.0 | ['image-classification', 'pytorch', 'onnx'] | false | Preprocessing config = model.default_cfg transform = Compose([ Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR), PILToTensor(), ConvertImageDtype(torch.float32), Normalize(config['mean'], config['std']) ]) input_tensor = transform(img).unsqueeze(0) | a48739016563238fce709a79802c632d |
apache-2.0 | ['image-classification', 'pytorch', 'onnx'] | false | Citation Original paper ```bibtex @article{DBLP:journals/corr/HeZRS15, author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun}, title = {Deep Residual Learning for Image Recognition}, journal = {CoRR}, volume = {abs/1512.03385}, year ... | c8e518c88c11daf92e3a805b9171c275 |
apache-2.0 | ['generated_from_trainer'] | false | blinding This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7158 - Accuracy: 0.6842 | 906b2d4a9c8ea2dcfa6f5f6d52409229 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 6.0 | 9944e4e81ed1bcce6de21a62b12025c1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9949 | 2.0 | 20 | 0.9573 | 0.4737 | | 0.5907 | 4.0 | 40 | 0.9047 | 0.5789 | | 0.2675 | 6.0 | 60 | 0.7158 | 0.... | 787c8188bd087a34b8a6e4465f0ce2f1 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 3.0 | f0a5e6b338e280523b2c185cc3ed778e |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_g... | c352df19c374c6bdb36e97dadd71b2f7 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2_murad_with_some_new_data This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the cvbn dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2971 - eval_wer: 0.2084 - eval_runtime: 511.5492 - eval_samples_per_s... | 4e7533675ed49117b5007a945bf7f7da |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision... | bcf1cbe183e160677f7707b1f740bf74 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-checkpoint-12 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-11.1](https://huggingface.co/jiobiala24/wav2vec2-base-checkpoint-11.1) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.0795 - Wer: 0.3452 | 4f7029f9472686e4a56b748a00719b1a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - 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: 1000 - num_epochs: 30 - mixed_precision_t... | dc0bb93dd5b66c5ce72c380cede03034 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.2793 | 1.64 | 1000 | 0.5692 | 0.3518 | | 0.2206 | 3.28 | 2000 | 0.6127 | 0.3460 | | 0.1733 | 4.93 | 3000 | 0.6622 | 0.358... | 7658d2baba0d7151f6602de6cdae6200 |
openrail | [] | false | This model card is a copy-paste from https://www.reddit.com/r/StableDiffusion/comments/ybavif/wikihow_db_model_entirely_free_model_trained_with/ The template is not 100% accurate and sometimes creates erroneous images, but it is incomparable to the natural quality of SD. The images used for training were all CC from ... | 3ba5c6dc22abc1948633eb8ffe28dc89 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_gender_male-2_female-8_s772 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure t... | e8a179ce3979af569d025762896897db |
creativeml-openrail-m | ['tune-a-video', 'text-to-video', 'diffusers'] | false | Usage Clone the [github repo](https://github.com/showlab/Tune-A-Video) ```bash git clone https://github.com/showlab/Tune-A-Video.git ``` Run inference code ```python from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline from tuneavideo.models.unet import UNet3DConditionModel from tuneavideo.util im... | 06d46259063d5485d40c69917c415c48 |
creativeml-openrail-m | ['tune-a-video', 'text-to-video', 'diffusers'] | false | Related Papers: - [Tune-A-Video](https://arxiv.org/abs/2212.11565): One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation - [Stable Diffusion](https://arxiv.org/abs/2112.10752): High-Resolution Image Synthesis with Latent Diffusion Models | 1ec2352df71cae7486bfb3679bd86a78 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'br', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | XLS-R-300M - Breton This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BR dataset. It achieves the following results on the evaluation set: - Loss: NA - Wer: NA | a3a009f855fa83c4b23da99d2c7ed817 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'br', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id infinitejoy/wav2vec2-large-xls-r-300m-breton-cv8 --dataset mozilla-foundation/common_voice_8_0 --config br --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ``... | e232e4c903e5b01dffc5205a39440bfc |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'br', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Inference With LM ```python import torch from datasets import load_dataset from transformers import AutoModelForCTC, AutoProcessor import torchaudio.functional as F model_id = "infinitejoy/wav2vec2-large-xls-r-300m-breton-cv8" sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "br", split="test... | 54f3623fa70ba6e7cfbe8cc363d0c6ab |
mit | ['unconditional-image-generation'] | false | Fashion MNIST unconditional Unet Model trained using DDPM Model Hyperparams: - Model size: 51,834,625 params - 3 stages: 128, 256, 512 channels - Linear Attention in 2nd and 3rd stages, Self Attention in Middle Stage - Optimizer: Adam - LR: 3e-4 - Batch Size: 64 - Grad Accumulation: 8 steps - Effectibe Batch Size: 51... | cade09981a2ada504e83bfd23ef52826 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-banking-1000-16-5-oos 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: 4.1313 - Accuracy: 0.3451 | c82b0f6a1711742176f1b3fe1660c6f1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 5.0616 | 1.0 | 1 | 4.7687 | 0.2035 | | 4.4657 | 2.0 | 2 | 4.5386 | 0.2920 | | 4.0496 | 3.0 | 3 | 4.3450 | 0.... | c3e7feeedc7d78cbcab5bfca186bd3f8 |
mit | ['summarization'] | false | BART for Gigaword - This model was created by fine-tuning the `facebook/bart-large-cnn` weights (also on HuggingFace) for the Gigaword dataset. The model was fine-tuned on the Gigaword training set for 3 epochs, and the model with the highest ROUGE-1 score on the training set batches was kept. - The BART Tokenizer f... | 6e4928965f7b54e8335d9432923ffcfc |
mit | ['summarization'] | false | Summary generation - This model achieves ROUGE-1 / ROUGE-2 / ROUGE-L of 37.28 / 18.58 / 34.53 on the Gigaword test set; this is pretty good when compared to PEGASUS, `google/pegasus-gigaword`, which achieves 39.12 / 19.86 / 36.24. - To achieve these results, generate text using the code below. `text_list` is a list ... | fd43d50d1094b14648af4d7e3261ec62 |
mit | [] | false | Liminalspaces on Stable Diffusion This is the `<liminal image>` 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 ca... | d7f08406d1729b45f92050e7ca4db6be |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_unispeech-ml_s610 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-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... | 209c4d0e8d8bfe8e75a4c7328e337463 |
mit | ['pytorch', 'ner', 'text generation', 'seq2seq'] | false | Inference ```shell git clone https://github.com/ovbystrova/InstructionNER cd InstructionNER ``` ```python from instruction_ner.model import Model model = Model( model_path_or_name="olgaduchovny/t5-base-ner-mit-movie", tokenizer_path_or_name="olgaduchovny/t5-base-ner-mit-movie" ) options = [ "ACTOR",... | da77bf312149b79ca3876ccec29566e2 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-squad-coffee20230108 This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3444 | 52f69d8a291946845a5f9b98c38daed7 |
apache-2.0 | ['generated_from_trainer'] | 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 15 | 75eb8e1bfeb09e414904e51d91873b5a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 89 | 1.9198 | | 2.3879 | 2.0 | 178 | 1.8526 | | 1.5528 | 3.0 | 267 | 1.8428 | | 1.1473 | 4.0 | 356 | 2.4035 ... | 7901ce0207583d4b601e2e186d3f8733 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_stsb_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 1.1533 - Pearson: 0.0554 - Spearmanr: 0.0563 - Combined Score: ... | cf01f9b618630bb348a4998276869bc2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 2.5973 | 1.0 | 45 | 1.2342 | -0.0353 | -0.0325 | -0.0339 | | 1.0952 | 2.0 | 90 ... | ec245f18f5a8a8929814c3d92a586d8e |
mit | ['vision', 'video-classification'] | false | X-CLIP (base-sized model) X-CLIP model (base-sized, patch resolution of 16) trained in a few-shot fashion (K=2) on [HMDB-51](https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https:... | 13a84b6e3a16d3cff177de8d6e5e9bb5 |
mit | ['vision', 'video-classification'] | false | Model description X-CLIP is a minimal extension of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs.  to look for fine-tuned versions on a task that interests you. | b3fda0ce4d1b3e1c9b533456a3338233 |
mit | ['vision', 'video-classification'] | false | L285). During validation, one resizes the shorter edge of each frame, after which center cropping is performed to a fixed-size resolution (like 224x224). Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation. | d86150089960c4bc992c8820aa4f1d78 |
apache-2.0 | ['translation', 'wmt19', 'facebook'] | false | Model description This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for en-ru. For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616). The abbreviation FSMT stands for Fai... | b567af76a3eedd46d175fd78680309d9 |
apache-2.0 | ['translation', 'wmt19', 'facebook'] | false | How to use ```python from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "facebook/wmt19-en-ru" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "Machine learning is great, isn't it?" input_ids = tokenizer.encode(input, retu... | a29b199802f853fa344e34f2592e1f13 |
apache-2.0 | ['translation', 'wmt19', 'facebook'] | false | Limitations and bias - The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981) | 4952999deaa2aa645f7a1020a07a583b |
apache-2.0 | ['translation', 'wmt19', 'facebook'] | false | Eval results pair | fairseq | transformers -------|---------|---------- en-ru | [36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724) | 33.47 The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support: - model ensemble, therefore the best performing chec... | ffef72cf519860156f780e6ee67f0afe |
apache-2.0 | ['translation', 'wmt19', 'facebook'] | false | BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020}, title={Facebook FAIR's WMT19 News Translation Task Submission}, author={Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}, booktitle={Proc. of WMT}, } ``` | ba77f6a849f7db05307ebddc0af4e1dc |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | t5-small-finetuned-xsum-finetuned-bioMedv3 This model is a fine-tuned version of [PeterBanning71/t5-small-finetuned-xsum](https://huggingface.co/PeterBanning71/t5-small-finetuned-xsum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.1056 - Rouge1: 4.8565 - Rouge2: 0.4435 - Roug... | 6dd2ea88c6abc7ff7c7469c34286dacc |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.