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 | ['automatic-speech-recognition', 'sv-SE'] | false | exp_w2v2t_sv-se_vp-nl_s615 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | a7cafa44a347c2e5ff55e789b2099ec9 |
apache-2.0 | ['relation extraction'] | false | 使用方法 ```commandline pip install lightningnlp ``` ```python from pprint import pprint from lightningnlp.task.relation_extraction import RelationExtractionPipeline pipline = RelationExtractionPipeline(model_name_or_path="xusenlin/duie-gplinker", model_name="gplinker", model_type="bert") text = "查尔斯·阿兰基斯（Charles Aráng... | cb0db65ee89de46a017a008bc9e15684 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr0_3e-05_all_27_02_2022-22_36_26 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: 0.6071 - Accuracy:... | 67f56c1b0284e0e00a3d9828d8246693 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.3920 | 0.7988 | 0.8624 | | No log | 2.0 | 390 | 0.3873 | 0.8171 | 0.8739 | | 0.3673 |... | 9fe88e0f10770912405990aba2d46563 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-6front-1body-6rear This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4394 - Wer: 0.1704 - Mer: 0.1647 - Wil: 0.2508 - Wip: 0.7492 - Hits: 55836 - S... | 2d70d1bf9449f73557037d5b40b5f78c |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.6164 ... | 849a45562334aef23831c3f9acdc62fe |
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: - Loss: 0.2243 - Accuracy: 0.919 - F1: 0.9193 | 80a70b7aa3b2bc6fc2cda87b194a7eb2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.833 | 1.0 | 250 | 0.3188 | 0.9015 | 0.8975 | | 0.2513 | 2.0 | 500 | 0.2243 | 0.919 | 0.9193 | | 57253554ff876f7303f576bcd1728c41 |
apache-2.0 | ['translation'] | false | opus-mt-fi-pon * source languages: fi * target languages: pon * OPUS readme: [fi-pon](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-pon/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | a6b9238ff0378ffcda773292f4845f42 |
apache-2.0 | ['generated_from_keras_callback'] | false | liyingz/bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0227 - Validation Loss: 0.0646 - Epoch: 2 | c586fd1de189c471aceda2a63a2ae5c9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1322 | 0.0585 | 0 | | 0.0396 | 0.0578 | 1 | | 0.0227 | 0.0646 | 2 | | 4d13b9c7b81e166362742ad24890591d |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | article2KW_test1_barthez-orangesum-title_finetuned_for_summurization This model is a fine-tuned version of [moussaKam/barthez-orangesum-title](https://huggingface.co/moussaKam/barthez-orangesum-title) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2895 - Rouge1: 0.2048 - Rou... | bceb3d20b5a4177184d2bf3b3bab38b9 |
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: 3 | 9bde9b2fe37e43101340753bec5e1f48 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:| | 0.4512 | 1.0 | 3368 | 0.3433 | 0.2030 | 0.0642 | 0.2037 | 0.2033 | | 0.3162 | 2.0 | 67... | 0384d28ee54c72f1a12057ab3a88a17f |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 2.4413 - Rouge1: 22.6804 - Rouge2: 8.3299 - Rougel: 17.9992 - Rougelsum: 20.7342 | 7cbb5250a2d82cd59a3277b0fa21c5d4 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 7.77 | 1.0 | 240 | 2.7230 | 17.25 | 5.629 | 14.0381 | 15.8959 | | 3.7586 | 2.0 |... | 62a45aa85eaa5afef0e78eab0ea67351 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | cos801-802-hf-workshop-mt5-small This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 2.7998 - Rouge1: 20.928 - Rouge2: 6.3239 - Rougel: 17.4455 - Rougelsum: 17.4566 | c872671aebeed239e2aa57323ab49e59 |
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: 1 | c61b502a5a3ee09a6e789576e8d0d7a7 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:---------:| | 3.844 | 1.0 | 1975 | 2.7998 | 20.928 | 6.3239 | 17.4455 | 17.4566 | | f7cb668e0e5ef994a545a32485fff044 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Hi - Swedish This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 5.4300 - eval_wer: 104.6248 - eval_runtime: 1806.3793 - eval_samples_per_second: ... | b06119002f1502b8c20f202101bb8e4c |
apache-2.0 | ['xlm-roberta-large', 'semantic role labeling', 'finetuned'] | false | Model description
This model is the [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) fine-tuned on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data. This is part of a project from which resulted the following models:
* [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaa... | bf946275124fae88557c6ad642dc95bf |
apache-2.0 | ['xlm-roberta-large', 'semantic role labeling', 'finetuned'] | false | How to use
To use the transformers portion of this model:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("liaad/srl-en_xlmr-large")
model = AutoModel.from_pretrained("liaad/srl-en_xlmr-large")
```
To use the full SRL model (transformers portion + a ... | b6ab5e53e682a64f892a634452aff500 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_logit_kd_mrpc This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5529 - Accuracy: 0.6838 - F1: 0.8122 - Combined Score: 0.7480 | b8a5c7668bde00121bcfecae8b3f98e7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6239 | 1.0 | 29 | 0.5558 | 0.6838 | 0.8122 | 0.7480 | | 0.6109 | 2.0 | 58 | 0.55... | a25e345765b35b7c53241fb949a1f77d |
apache-2.0 | [] | false | Model Description This is the ClimateBERT language model based on the DIV-SELECT and SIM-SELECT sample selection strategy. *Note: We generally recommend choosing the [distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model over this language model (unless you hav... | a2a722a1dcc43dd53fa44cdbe2d90ab7 |
apache-2.0 | [] | false | Climate performance model card | distilroberta-base-climate-d-s | | |--------------------------------------------------------------------------|----------------| | 1. Is the resulting model publicly available? | Yes | | 2... | ebb42957cb5881384f7ca84d03c18463 |
mit | [] | false | Model description Carptriever-1 is a `bert-large-uncased` retrieval model trained with contrastive learning via a momentum contrastive (MoCo) mechanism following the work of G. Izacard et al. in ["Contriever: Unsupervised Dense Information Retrieval with Contrastive Learning"](https://arxiv.org/abs/2112.09118). | 964bf3c232432455b1183616fcf9d9e5 |
mit | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel def mean_pooling(token_embeddings, mask): token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.) sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None] return sentence_embeddings | 01185535379611640d586b0038d6d642 |
mit | [] | false | Remove pooling layer model = AutoModel.from_pretrained("CarperAI/carptriever-1", add_pooling_layer=False) tokenizer = AutoTokenizer.from_pretrained("CarperAI/carptriever-1") sentences = [ "Where was Marie Curie born?", | ea344e4746f96d41353023852c8b7142 |
mit | [] | false | Query "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.", "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace." ] | 9c00e4dc2b38a78fd0b48c7e95b2c723 |
mit | [] | false | Compute dot-product scores between the query and sentence embeddings query_embedding, sentence_embeddings = embeddings[0], embeddings[1:] scores = (query_embedding @ sentence_embeddings.transpose(0, 1)).cpu().tolist() sentence_score_pairs = sorted(zip(sentences[1:], scores), reverse=True) print(f"Query: {sentences[0]... | bbb644d63534137b02ce29b37986aa9a |
mit | [] | false | Training data Carptriever-1 is pre-trained on a de-duplicated subset of [The Pile](https://pile.eleuther.ai/), a large and diverse dataset created by EleutherAI for language model training. This subset was created through a [Minhash LSH](http://ekzhu.com/datasketch/lsh.html) process using a threshold of `0.87`. | 006d29edb857e3e93672b8776a80bc7a |
mit | [] | false | Training procedure The model was trained on 32 40GB A100 for approximately 100 hours with the following configurations: - Base model: - `bert-large-uncased` - Optimizer settings: - `optimizer = AdamW` - `lr = 1e-5` - `schedule = linear` - `warmup = 20,000 steps` - `batch size ... | 7ef99ca51d29ee8afde4de6c4abff63b |
mit | [] | false | [BEIR: Benchmarking IR](https://github.com/beir-cellar/beir) We report the following BEIR scores as measured in normalized discounted cumulative gain (nDCG@10): | Model | Avg | MSMARCO | TREC-Covid | NFCorpus | NaturalQuestions | HotpotQA | FiQA | ArguAna | Tóuche-2020 | Quora | CQAdupstack | DBPedia | Sci... | f5b77f7b78d6a02ffbf32c4bd2dd9b46 |
mit | [] | false | [CodeSearchNet Challenge Evaluating the State of Semantic Code Search](https://arxiv.org/pdf/1909.09436.pdf) We provide results on the CodeSearchNet benchmark, measured in Mean Reciprocal Rank (MRR), following the code search procedure outlined in Section 3.3 of Neelakantan et al.'s ["Text and Code Embeddings by Cont... | 2fe074b253bda9b97dd460be6accd14e |
mit | [] | false | Acknowledgements This work would not have been possible without the compute support of [Stability AI](https://stability.ai/). Thank you to Louis Castricato for research guidance and Reshinth Adithyan for creating the CodeSearchNet evaluation script. | 6309358d2afa00f7e079f8eace2dd3d0 |
mit | [] | false | Citations ```bibtex @misc{izacard2021contriever, title={Unsupervised Dense Information Retrieval with Contrastive Learning}, author={Gautier Izacard and Mathilde Caron and Lucas Hosseini and Sebastian Riedel and Piotr Bojanowski and Armand Joulin and Edouard Grave}, year={2021}, url = {https://arxiv.... | b41af00baee04faab4c25d926d535876 |
apache-2.0 | ['translation'] | false | opus-mt-ceb-fr * source languages: ceb * target languages: fr * OPUS readme: [ceb-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ceb-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | 2d18e64f7875e7c9e60197f5cfc4a06f |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_no-pretraining_s834 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has bee... | 26f410c74f9b7dc12c52a21d4f43f21c |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-Test 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: 0.2703 - Accuracy: 0.904 - F1: 0.9048 | 8444872269089aae7412edba3ec0aae3 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-wandb-week-3-complaints-classifier-1024 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the consumer-finance-complaints dataset. It achieves the following results on the evaluation set: - Loss: 0.5351 - Accuracy: 0.8280 - F1: 0.8237 - Recall:... | 6e15730d9b5891e6af14e67ea6761f1d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9.027176214786854e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1024 - num_epochs: 2 - mi... | 188cf04dde7776b3da4b324ca6c2b1c2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.7756 | 0.61 | 1500 | 0.7411 | 0.7647 | 0.7375 | 0.7647 | 0.7606 | | 0.5804 | 1.22 |... | 33c8458c3ff65289472d37a6d930dc4d |
apache-2.0 | ['generated_from_trainer'] | false | Article_250v8_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article250v8_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.3329 - Precision: 0.4216 - Recall: 0.3991 - F1: 0.4100 - Accuracy: ... | 5b3af64ed814c6312ad9b288f1610f6a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 28 | 0.5293 | 0.1767 | 0.0454 | 0.0722 | 0.7988 | | No log | 2.0 |... | 0e2218735824437fe15415bd1fb29759 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | sumer Dreambooth model trained by taranarora 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-diff... | 368cda3d53bae54a1a84044d07e21fb0 |
mit | ['generated_from_trainer'] | false | detect-femicide-news-xlmr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0161 - Accuracy: 0.9973 - Precision Neg: 0.9975 - Precision Pos: 0.9967 - Recall Neg: 0.9988 - Recall Po... | 538396421d068e117ee56645e68f52d2 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 8 - seed: 1996 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 | 0afe2c8bca9e95d1d30b75b91d7cced7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Neg | Precision Pos | Recall Neg | Recall Pos | F1 Score Neg | F1 Score Pos | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:-------------:|:----------:|:----------:|:------------:|:------------:| |... | 4775020b5663fc38855a1b4fa481419a |
mit | [] | false | PyramidheadCosplay on Stable Diffusion This is the `<Cos-Pyramid>` 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... | 3b650f8535a5068ae11663470355c7ef |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_data_aug_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: 2.7704 - Pearson: 0.1811 - Spearmanr: 0.1984 - Combined Score: ... | ddb11339cd5dfc9201f2dc610dbd6239 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------:|:--------------:| | 1.1017 | 1.0 | 2518 | 2.7704 | 0.1811 | 0.1984 | 0.1898 | | 0.654 | 2.0 | 50... | a7a1a51e4c43cbf09ef512897abd2039 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-work-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: 2.3586 - Accuracy: 0.3689 | e7e81eb6ee0f81741c51422187e0f727 |
mit | ['keyphrase-generation'] | false | 🔑 Keyphrase Generation model: T5-small-OpenKP Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first do... | a295140d9471e06aea93415dbdbcbe90 |
mit | ['keyphrase-generation'] | false | 📓 Model Description This model uses [T5-small model](https://huggingface.co/t5-small) as its base model and fine-tunes it on the [OpenKP dataset](https://huggingface.co/datasets/midas/openkp). Keyphrase generation transformers are fine-tuned as a text-to-text generation problem where the keyphrases are generated. The... | 21f9b6cc7e2d0e65b035fda9e8ddd084 |
mit | ['keyphrase-generation'] | false | Load pipeline model_name = "ml6team/keyphrase-generation-t5-small-openkp" generator = KeyphraseGenerationPipeline(model=model_name) ``` ```python text = """ Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand... | a2aa0033c664e957bfd4255a196f9b08 |
mit | ['keyphrase-generation'] | false | 📚 Training Dataset [OpenKP](https://github.com/microsoft/OpenKP) is a large-scale, open-domain keyphrase extraction dataset with 148,124 real-world web documents along with 1-3 most relevant human-annotated keyphrases. You can find more information in the [paper](https://arxiv.org/abs/1911.02671). | 6906e6841c6580dfd9716dde89381c57 |
mit | ['keyphrase-generation'] | false | Dataset parameters dataset_full_name = "midas/inspec" dataset_subset = "raw" dataset_document_column = "document" keyphrase_sep_token = ";" def preprocess_keyphrases(text_ids, kp_list): kp_order_list = [] kp_set = set(kp_list) text = tokenizer.decode( text_ids, skip_special_tokens=True, clean_up_to... | 4b99aee260b518b2aef0fa51302b7ff9 |
mit | ['keyphrase-generation'] | false | 📝 Evaluation Results Traditional evaluation methods are the precision, recall and F1-score @k,m where k is the number that stands for the first k predicted keyphrases and m for the average amount of predicted keyphrases. In keyphrase generation you also look at F1@O where O stands for the number of ground truth keyp... | 9caa206599473f1f17a2671ebe5153ee |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-sst2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2716 - Accuracy: 0.9266 | 3e7ce7be7094507791ab3f9af96a244c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.1666 | 1.0 | 2105 | 0.2403 | 0.9232 | | 0.1122 | 2.0 | 4210 | 0.2716 | 0.9266 | | 0.0852 | 3.0 | 6315 | 0.3150 ... | 0042cc8a838a3b178a9ad5f19e728770 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | glpn-nyu-finetuned-diode-230124-035129 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.4346 - Mae: 0.4251 - Rmse: 0.6137 - Abs Rel: 0.4412 - Log Mae: 0.1720 - Log Rmse:... | 364668ea51b6211237ed56b6a43d4e62 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 1.0761 | 1.0 | 72 | 0.5029 ... | 075a57a5477c22fc02024de410092e35 |
apache-2.0 | [] | false | Results on Web Questions - Test Set |Id | link | Exact Match | |---|---|---| |T5-11b|https://huggingface.co/google/t5-11b-ssm-wq|44.7| |**T5-xxl**|**https://huggingface.co/google/t5-xxl-ssm-wq**|**43.5**| | af392bdb8e8205dfada87a83cabe1467 |
apache-2.0 | [] | false | Usage The model can be used as follows for **closed book question answering**: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-xxl-ssm-wq") t5_tok = AutoTokenizer.from_pretrained("google/t5-xxl-ssm-wq") input_ids = t5_tok("When ... | fdb81b06ddb0e8bf51fab69f9a25b662 |
mit | ['generated_from_trainer', 'ner', 'bert'] | false | xlm-roberta-ner-japanese (Japanese caption : 日本語の固有表現抽出のモデル) This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) (pre-trained cross-lingual ```RobertaModel```) trained for named entity recognition (NER) token classification. The model is fine-tuned on NER dataset provid... | 99b1dbdd10a898b111d021e59ae71515 |
mit | ['generated_from_trainer', 'ner', 'bert'] | false | Intended uses ```python from transformers import pipeline model_name = "tsmatz/xlm-roberta-ner-japanese" classifier = pipeline("token-classification", model=model_name) result = classifier("鈴木は4月の陽気の良い日に、鈴をつけて熊本県の阿蘇山に登った") print(result) ``` | ef0b99b9ba72f4c72914af665c393d1a |
mit | ['generated_from_trainer', 'ner', 'bert'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | c68697019f69901189199c885481cc15 |
mit | ['generated_from_trainer', 'ner', 'bert'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 446 | 0.1510 | 0.8457 | | No log | 2.0 | 892 | 0.0626 | 0.9261 | | No log | 3.0 | 1338 | 0.0366 | 0.9580 | |... | c24fd611431d6794d16299456603e363 |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pretrained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [deberta-base-japanese-wikipedia](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-wikipedia) and [UD_Japanese-GSDLUW](https://gi... | 605b55e5b7c027b44c290ccafa0b60db |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'pos', 'dependency-parsing'] | false | text = "+text+"\n" v=[(s,e) for s,e in w["offset_mapping"] if s<e] for i,(s,e) in enumerate(v,1): q=self.model.config.id2label[p[i,h[i]]].split("|") u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n" return... | 4ce203359729247929bd765258ccf432 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Intended uses & limitations The model is intended to be used as a universal sentence encoder, similar to [Google's Universal Sentence Encoder](https://tfhub.dev/google/universal-sentence-encoder/4) or [Sentence Transformers](https://github.com/UKPLab/sentence-transformers). | 2a608417ca0bde3764518ad2b20d46a1 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Prepare some text to embed text = [ "A smiling costumed woman is holding an umbrella.", "A happy woman in a fairy costume holds an umbrella.", ] inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt") | f7d57b1951135b2ddeb412e2a642e515 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_accent_surpeninsular-8_nortepeninsular-2_s571 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 ... | 3eaca12babab8c8a2ab03eca8678b7e4 |
apache-2.0 | ['generated_from_keras_callback'] | false | Haakf/allsides_right_text_conc 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: - Train Loss: 2.1626 - Validation Loss: 2.0436 - Epoch: 5 | a00fe90a91c362ddc0809f857e62c21a |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'Polynomia... | 7527f332614be8370d60debd92fe3647 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.2589 | 2.1120 | 0 | | 2.2360 | 2.1708 | 1 | | 2.2179 | 2.0876 | 2 | | 2.1961 | 2.0284 | 3 | | 2.1851 | 2.0921 | 4 | | 2.1626 |... | 22f53a7341b07f627f12fe05832d2867 |
apache-2.0 | ['generated_from_keras_callback'] | false | test-summarization This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.6449 - Validation Loss: 2.8528 - Epoch: 0 | 91ea329306768e8a567836f1cedd33e6 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-TINY-NL6 (Deep-Narrow version) T5-Efficient-TINY-NL6 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and ... | 16362a7d212cc9e053fb0ea6ab03e678 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-tiny-nl6** - is of model type **Tiny** with the following variations: - **nl** is **6** It has **19.26** million parameters and thus requires *ca.* **77.03 MB** of memory in full precision (*fp32*) or **38.52 MB** of memory in half precision (*fp16*... | 246abdefdaaaa8f1b46a48b9f48a07eb |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | skmcm Dreambooth model trained by anmol-chawla 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-di... | 86c1497471ed5478b441dc5bd7f487a8 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_cooking-roberta-large-v1-5-4 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contr... | 38f5a9a1f708338f949fd359ad024b66 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-speech'] | false | Riffusion Riffusion is an app for real-time music generation with stable diffusion. Read about it at https://www.riffusion.com/about and try it at https://www.riffusion.com/. * Web app: https://github.com/hmartiro/riffusion-app * Inference server: https://github.com/hmartiro/riffusion-inference * Model checkpoint: ... | 66ee76eafb45c2f92170814dbe6cd75b |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-speech'] | false | Citation If you build on this work, please cite it as follows: ``` @software{Forsgren_Martiros_2022, author = {Forsgren, Seth* and Martiros, Hayk*}, title = {{Riffusion - Stable diffusion for real-time music generation}}, url = {https://riffusion.com/about}, year = {2022} } ``` | 8835cde82f365d2facb93df3c579e66f |
mit | ['audio-generation'] | false | !pip install diffusers[torch] accelerate scipy from diffusers import DiffusionPipeline from scipy.io.wavfile import write model_id = "harmonai/glitch-440k" pipe = DiffusionPipeline.from_pretrained(model_id) pipe = pipe.to("cuda") audios = pipe(audio_length_in_s=4.0).audios | fef601e03612c530ccc4e330f7be3a34 |
mit | ['audio-generation'] | false | !pip install diffusers[torch] accelerate scipy from diffusers import DiffusionPipeline from scipy.io.wavfile import write import torch model_id = "harmonai/glitch-440k" pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") audios = pipeline(audio_length_in_s=4.0).audios... | 068f6e2867cd37e3515093f152480e53 |
mit | ['russian'] | false | This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only Russian and some English embeddings left. * The original model has 582M parameters, with 384M of them being input and output embeddings. * After shrinking the `sentencepiece` vocabulary from 250K to 30K (top 10... | 81f997541be1439d837fded7e549fca3 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_20k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 20k 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 r... | 4bff294abf0539260d90271a4dea0237 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_20k'] | 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_20k') model = TFBertModel.from_pretrained("google/multiber... | 789f420865d3e24ff1fb44e8570ad73b |
afl-3.0 | [] | false | This model has been trained by the original authors of the paper [(Comet-) Atomic 2020: On Symbolic and Neural Commonsense Knowledge Graphs.](https://www.semanticscholar.org/paper/COMET-ATOMIC-2020%3A-On-Symbolic-and-Neural-Knowledge-Hwang-Bhagavatula/e39503e01ebb108c6773948a24ca798cd444eb62) and has been released [her... | 90ed84120ac6956704f49701cfd7e739 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr 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: 0.2961 - F1: 0.8330 | 1c4513797b78cc52c6c03edd221f67f6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5464 | 1.0 | 287 | 0.3304 | 0.7912 | | 0.2617 | 2.0 | 574 | 0.2995 | 0.8142 | | 0.1672 | 3.0 | 861 | 0.2961 | 0.8330 | ... | 205637a3be11384f5fef543f6f0a9cbd |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | summary_tutorial This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 2.8504 - Rouge1: 19.4358 - Rouge2: 10.0475 - Rougel: 18.6327 - Rougelsum: 18.648 | da922e55cf09a5fd7c6f88813b269578 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 2.867 | 1.0 | 771 | 2.7687 | 18.7278 | 10.8323 | 17.9617 | 17.8262 | | 2.5363 | 2.0 ... | 7a222ad32fcdd4cdd1dd8cad2fb78402 |
mit | [] | false | painted_by_silver_of_999 on Stable Diffusion This is the `<cat-toy>` 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. Y... | 1b024d29493f517485ed9aa5bdcb8fcc |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-becas-5 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 4.8805 | ebc27cac5c76fe38a919da202c88122a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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: 10 | 55f06bce1e9832901d267aeab6f22d4a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 5 | 5.4344 | | No log | 2.0 | 10 | 4.9002 | | No log | 3.0 | 15 | 4.3601 | | No log | 4.0 | 20 | 4.4784 ... | 42a0e5fc04a35679f4ecefb914457d20 |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch', 'sentence-similarity'] | false | stjiris/bert-large-portuguese-cased-legal-tsdae-nli-sts-v0 (Legal BERTimbau) This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. stjiris/bert-large-portuguese-cased-legal-ts... | 07287d4986b0b2c82547fb46c8627c4d |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch', 'sentence-similarity'] | 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 = ["Isto é um exemplo", "Isto ... | 9778b9c99936281d54baf90df5cf9796 |
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