license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | [] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-micro | F1-macro | F1-weighted | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|:---------:|:------:| | 0.9154 | 1.0 | 1812 | 0.8984 | 0.6785 | 0... | 7e3cdf4d32d06bdd9cb250be6358d6b1 |
mit | [] | false | Validation evaluation | Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score | |----------------|----------------|----------------|-------------------| | PolicyBERTa-7d | 0.71 | 0.67 | 0.70 | | e72b60f0799f7fb16652418d8e03fe05 |
mit | [] | false | Test evaluation | Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score | |----------------|----------------|----------------|-------------------| | PolicyBERTa-7d | 0.65 | 0.60 | 0.65 | | 927de2823271805dda7b18440cb223b7 |
mit | [] | false | Evaluation per category | Label | Validation F1-Score | Test F1-Score | |-----------------------------|---------------------|---------------| | external relations | 0.76 | 0.70 | | freedom and democracy | 0.61 | 0.55 | | political sy... | 93a968b3ddb98cf440d66f2296b29905 |
mit | [] | false | Evaluation based on saliency theory Saliency theory is a theory to analyse politial text data. In sum, parties tend to write about policies in which they think that they are seen as competent. Voters tend to assign advantages in policy competence in line to the assumed ideology of parties. Therefore you can analyze t... | b3408747691d6c0402ab482b951adef9 |
mit | [] | false | measuring-parties-left-right-positions). But PolicyBERTa isn't fine-tuned to predict the rile-index, if you're interested in that, check [ManiBERT](https://huggingface.co/niksmer/ManiBERT) or [RoBERTa-RILE](https://huggingface.co/niksmer/RoBERTa-RILE). In the following table, the predicted and original share of the in... | 8fda26d64610936e6474c1d1f3bfd91c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-model-4 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 3.1544 | 8cc4fd7e7a28b32bcb4599e710421b76 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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: 4 | eb9cb1ad92a026546a1f1755f0f7d1c0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7789 | 1.0 | 554 | 2.0564 | | 1.4826 | 2.0 | 1108 | 2.0916 | | 0.7706 | 3.0 | 1662 | 2.4116 | | 0.3379 | 4.0 | 2216 | 3.1544 ... | aa777084798c75a1e35b42bb9c4a1554 |
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 dataset. It achieves the following results on the evaluation set: - Loss: 1.1582 | 8665fefd6039780f5a49507998e6cd59 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2176 | 1.0 | 5533 | 1.1429 | | 0.9425 | 2.0 | 11066 | 1.1196 | | 0.7586 | 3.0 | 16599 | 1.1582 | | 415246a3e61a48ec839cf2f800d835aa |
apache-2.0 | ['generated_from_trainer'] | false | new-test-model This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0962 - Precision: 0.9704 - Recall: 0.9766 - F1: 0.9735 - Accuracy: 0.9791 | 1f8facd2a69ed0e7d604ed608ae50ba9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 151 | 0.1872 | 0.9295 | 0.9405 | 0.9349 | 0.9535 | | No log | 2.0 |... | 67c01a3e7c11ae3fc1a15acc9234df8c |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Densely Residual Laplacian Super-Resolution (DRLN) DRLN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Densely Residual Laplacian Super-resolution](https://arxiv.org/abs/1906.12021) by Anwar et ... | 3128f4c3026e802526aa10290cfad7d1 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Model description Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images. However, existing algorithms often require very deep architectures and long training times. Furthermore, current convolutional neural networks for super-resolution are unable to explo... | e347edecdbd5ad62f4480272d048ecf4 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | How to use The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library: ```bash pip install super-image ``` Here is how to use a pre-trained model to upscale your image: ```python from super_image import DrlnModel, ImageLoader from PIL import Image import requests url = 'https://pa... | f5beb4fff1a6b1cf52488e8be4aed4d8 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Pretraining The model was trained on GPU. The training code is provided below: ```python from super_image import Trainer, TrainingArguments, DrlnModel, DrlnConfig training_args = TrainingArguments( output_dir='./results', | 6ee5e92828582b6eb42854ba990ffa10 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Algorithm). Evaluation datasets include: - Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5) - Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14) - BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100) - Urban100 - [Huang et al... | 10d7df2a4ad3ea9ef920a951ec8533c0 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | BibTeX entry and citation info ```bibtex @misc{wang2021bam, title={BAM: A Lightweight and Efficient Balanced Attention Mechanism for Single Image Super Resolution}, author={Fanyi Wang and Haotian Hu and Cheng Shen}, year={2021}, eprint={2104.07566}, archivePrefix={arXiv}, primaryClass={eess.IV... | 3365eddaee8de77dbc52654f2df11faa |
cc-by-4.0 | ['norwegian', 'bert'] | false | Description NB-BERT-large is a general BERT-large model built on the large digital collection at the National Library of Norway. This model is trained from scratch on a wide variety of Norwegian text (both bokmål and nynorsk) from the last 200 years using a monolingual Norwegian vocabulary. | 79f58f2ed875a4982788e0193927b6a5 |
cc-by-4.0 | ['norwegian', 'bert'] | false | Intended use & limitations The 1.0 version of the model is general, and should be fine-tuned for any particular use. Some fine-tuning sets may be found on Github, see * https://github.com/NBAiLab/notram | d147fcbb453d2d8ab678e14d48b8df71 |
mit | ['generated_from_trainer'] | false | edos-2023-baseline-roberta-base-label_category This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0133 - F1: 0.5792 | b295eb2f67b4606cbd3181d2ead76221 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.169 | 1.18 | 100 | 1.0580 | 0.2159 | | 0.9143 | 2.35 | 200 | 0.9283 | 0.5405 | | 0.7535 | 3.53 | 300 | 0.9387 | 0.5665 | |... | 9e496ac967fe80ab994dde524704ea77 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | ppaattaass Dreambooth model trained by Brainergy 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-... | 6930ec033ec06bbaf0edf84b92a079a2 |
mit | ['generated_from_trainer'] | false | dreamy_williams This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250... | acbed69abdfde421957d7799a0d53ac1 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tom... | 06088edd565a78a723ba52cf91a8a62c |
apache-2.0 | ['generated_from_trainer'] | false | correct_distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_42_32 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: 0.120... | 6bc30089bf7f86d9f9b8930f626ae703 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 15 | 0.1222 | 0.12 | 0.0139 | 0.0249 | 0.9736 | | No log | 2.0 |... | 505f6feac1f5eedad5231c21502e9ed6 |
apache-2.0 | ['translation'] | false | opus-mt-fr-tiv * source languages: fr * target languages: tiv * OPUS readme: [fr-tiv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-tiv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 21562450cec1204a6fa1d738b7466814 |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_700k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 0, Step 700k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different ... | 01dcdcc9aa445df32107fcf066bb0a32 |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_700k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_700k') model = TFBertModel.from_pretrained("google/multibe... | f1c0d1df37d953cb99a92646c2391f63 |
apache-2.0 | ['translation'] | false | opus-mt-sg-fi * source languages: sg * target languages: fi * OPUS readme: [sg-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sg-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://... | 72958e67be052153dfcc6a4c17fc8b11 |
apache-2.0 | ['generated_from_trainer'] | false | Heritage-in-Digital-Age-distilbert-base-uncased-expression-rating This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0133 - Accuracy: 0.3496 | efd8552315937f8b50dfe6a6f539929f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 18 | 1.9870 | 0.3333 | | No log | 2.0 | 36 | 1.9731 | 0.3415 | | No log | 3.0 | 54 | 2.0133 | 0.... | 4935978c0daae1f259b4c2e8f8d6dbda |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("allenai/macaw-11b") model = AutoModelForSeq2SeqLM.from_pretrained("allenai/macaw-11b") input_string = "$answer$ ; $mcoptions$ ; $question$ = What is the color of a cloudy sky?" input_ids = tok... | 8ddf50d63b7172f453db4d7f1fa1eebe |
apache-2.0 | [] | false | BibTeX entry and citation info ```bibtex @article{Tafjord2021Macaw, title={General-Purpose Question-Answering with {M}acaw}, author={Oyvind Tafjord and Peter Clark}, journal={ArXiv}, year={2021}, volume={abs/2109.02593} } ``` | 827a25df53109fb70838d8cdde2e7c10 |
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: 0.1397 - F1: 0.8610 | 56b271f47f2806fde7ced5d1eb193933 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2663 | 1.0 | 525 | 0.1628 | 0.8273 | | 0.1338 | 2.0 | 1050 | 0.1457 | 0.8396 | | 0.0844 | 3.0 | 1575 | 0.1397 | 0.8610 | ... | bed1a363cb5c66d8338894cd69d89753 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned_entailment_inference This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0249 | 1730258c144a392ebbb4a7e761a81b27 |
apache-2.0 | ['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: 20 - mixed_precision_training: Native AMP | 7b58fbfc28e1650e939ea2162aa2a9d4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2885 | 1.0 | 522 | 1.2005 | | 1.2209 | 2.0 | 1044 | 1.1594 | | 1.1871 | 3.0 | 1566 | 1.1263 | | 1.1455 | 4.0 | 2088 | 1.1098 ... | e3d6dec0ab593464e16d8d0a686b7afe |
apache-2.0 | ['translation'] | false | opus-mt-ig-de * source languages: ig * target languages: de * OPUS readme: [ig-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ig-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | 383d57957460871a2743c8b4627ea4bf |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | Model Description This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-large-japanese](https://huggingface.co/cl-tohoku/bert-large-japanese). Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech)... | 263609cde84abab73aa5db0d15261d79 |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | How to Use ```py import torch from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-large-japanese-unidic-luw-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-large-japanese-unidic-luw-upos") s="国境の長いトンネルを抜けると... | e6a82b2a33fc68bee427f4ea7a76136d |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/gtr-t5-large
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
This model was converted from the Tensorflow model [gtr-large-1](https://tf... | af76dd27b901c521eab2df03d86811ff |
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... | 73578cd6eebeba37269933de70cc6768 |
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/gtr-t5-large)
| fc3ea05fcc56748e076f10dc3f23cfa2 |
apache-2.0 | ['image-classification', 'vision'] | false | Data2Vec-Vision (base-sized model, pre-trained only) BEiT model pre-trained in a self-supervised fashion on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.or... | 3b96bce6e047e0417d42f35d312d8000 |
apache-2.0 | ['image-classification', 'vision'] | false | Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?other=data2vec-vision) to look for fine-tuned versions on a task that interests you. | 7c1439fd23230f97d056b368d981f6fa |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "tt", split="test[:2%]") processor = Wav2Vec2Processor.from_p... | 098c4b1917fbf69ba069e9c849e4f40e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Tatar test data of Common Voice. ```python import torch import torchaudio import urllib.request import tarfile import pandas as pd from tqdm.auto import tqdm from datasets import load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | 593ab6916ab6ec7422542e0a0e8fd11e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Download the raw data instead of using HF datasets to save disk space data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/tt.tar.gz" filestream = urllib.request.urlopen(data_url) data_file = tarfile.open(fileobj=filestream, mode="r|gz") data_file.e... | e4715347f26142db4d7977a5ea9cff44 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | remove repeated spaces sent = " ".join(sent.split()) return sent targets = [] preds = [] for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]): row["sentence"] = clean_sentence(row["sentence"]) speech_array, sampling_rate = torchaudio.load(clips_path + row["path"]) resampler = torchaudio... | 49cd38a1f8896dfe708fd7686b9f3470 |
apache-2.0 | ['generated_from_trainer'] | false | MTL-distilbert-base-uncased This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0874 | 5dda03047ea5e642a9c90612c9738212 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5593 | 1.0 | 99 | 2.3163 | | 2.4346 | 2.0 | 198 | 2.2918 | | 2.3377 | 3.0 | 297 | 2.2345 | | 2.2953 | 4.0 | 396 | 2.1463 ... | e9aa0256696fda23b044839432ba8be8 |
apache-2.0 | [] | false | distilbert-base-en-tr-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accur... | ce887709a3fa693dd9ff6a0401b158b7 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-tr-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-tr-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Gi... | ca4e9cacf590006782b255540ab75783 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_wnli_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3436 - Accuracy: 0.5634 | 1231c50ca2ac78a03b5759e3a36de153 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3503 | 1.0 | 3 | 0.3457 | 0.5634 | | 0.3494 | 2.0 | 6 | 0.3476 | 0.5634 | | 0.3469 | 3.0 | 9 | 0.3439 | 0.... | a43ba8e87ee861a595e3d529e0dafb14 |
mit | [] | false | saheeli-rai on Stable Diffusion This is the `<saheeli-rai>` 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 al... | db5e5d75ed3dc880467d1a0a1234e2b5 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-zle-it Neural machine translation model for translating from East Slavic languages (zle) to Italian (it). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the ... | f4413835a43ff6d7c7bf876df666aab6 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-19 * source language(s): bel rus ukr * target language(s): ita * model: transformer-big * data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807_transformer-big_2022-03-19.zip](h... | 4f7991dc5ebb82f2db6fdd601d64f3f7 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Вони не ідіоти.", "Я не хочу идти в банк." ] model_name = "pytorch-models/opus-mt-tc-big-zle-it" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_na... | 3b6de48fc313124c5b4601c024f6af33 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Non voglio andare in banca. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-it") print(pipe("Вони не ідіоти.")) | 2fa3278f1c6d7593af80eb4e1422bb70 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807_transformer-big_2022-03-19.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-ita/opusTCv20210807_transformer-big_2022-03-19.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-03-19.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zl... | 9cda9722c92e17052f1249d1dac1b8f4 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | bel-ita | tatoeba-test-v2021-08-07 | 0.65945 | 49.3 | 264 | 1681 | | rus-ita | tatoeba-test-v2021-08-07 | 0.64037 | 43.5 | 10045 | 71584 | | ukr-ita | tatoeba-test-v2021-08-07 | 0.69570 | 50.0 | 5000 | 27846 | | bel-ita | flores101-devtest | 0.46311 | 13... | 01dd5a725148f03c285a619db7c31d9f |
apache-2.0 | ['translation'] | false | opus-mt-guw-fi * source languages: guw * target languages: fi * OPUS readme: [guw-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/guw-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | b45fbeffdb8521a007c5bbda120eab89 |
mit | ['generated_from_trainer'] | false | finetuned_gpt2-xl_sst2_negation0.0001_pretrainedTrue_epochs1 This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 2.9187 | f8f5b4b605ff745c794cedc0a9d9c838 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-j-kana-colab 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_10_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7188 - Wer: 0.1285 | 763138e2947d2c0805c5b99c8981ecb7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 7e3135060dd14623242e432a1cfc9a2b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 397 | 3.8381 | 0.9571 | | No log | 2.0 | 794 | 0.8909 | 0.2265 | | 4.0962 | 3.0 | 1191 | 0.8076 | 0.2054 | |... | b48c7971549ced1d0657d3f6a9e338a4 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0616 - Precision: 0.9310 - Recall: 0.9497 - F1: 0.9403 - Accuracy: 0.9864 | 1413f95cd577a4bb2d36a5dc9c7d3331 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0884 | 1.0 | 1756 | 0.0697 | 0.9128 | 0.9283 | 0.9205 | 0.9819 | | 0.0322 | 2.0 |... | 60b69b40349ee44db17199fe1a18156c |
mit | ['generated_from_trainer'] | false | quirky_hamilton This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250... | b39b76e11aacb880cf63522519daf3f9 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tom... | eaf0b996bcc6af90894af6aa40db3133 |
apache-2.0 | ['generated_from_trainer'] | false | Switch Transformer (base-16) fine-tuned om samsum for conversation summarization This model is a fine-tuned version of [google/switch-base-16](https://huggingface.co/google/switch-base-16) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4434 - Rouge1: 47.2139 - Rouge2: 23.339... | 1f92e5a091565d64c69b432458ea7c54 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.846 | 1.0 | 3683 | 1.4857 | 45.9134 | 22.4258 | 38.9716 | 42.6169 |... | d3d681632244e56ef2a1f6a74bd302c9 |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;} | 73f48c925381ef54275b34a15d266af0 |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;} | b68777beb98bbbd3d7a553e5df179a68 |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. ... | 7f5a0a487bd80e9bbedc1bfae13676cb |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-text-repr-fallback {display: none;}</style><div id="sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>ComplementNB()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.<... | 75f8a40a8e9c90a717666c783147090c |
gpl-3.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Spanish-AnCora | Feature | Description | | --- | --- | | **Name** | `es_udv25_spanishancora_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimenta... | 6ae6da9856d86f7827a2dd9fb2e2098c |
gpl-3.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (2060 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `AUX_PRON`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRO... | 4d05b0c223d8edeaa7972c652722331c |
gpl-3.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.98 | | `TOKEN_P` | 99.98 | | `TOKEN_R` | 99.99 | | `TOKEN_ACC` | 100.00 | | `SENTS_F` | 97.99 | | `SENTS_P` | 97.43 | | `SENTS_R` | 98.55 | | `TAG_ACC` | 98.92 | | `POS_ACC` | 99.03 | | `MORPH_ACC` | 97.96 | | `DEP_UAS` | 93.99 | | `DEP_LAS` | 91.95 | | `LEMMA_... | ad8a6a024efeb1e3400633e338ec74a6 |
mit | ['generated_from_trainer'] | false | hardcore_albattani This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tom... | 714d14300675be164769e4029b483de6 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', ... | a0be85291ee84b1a13a8948085a6b909 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | xls-r-300m-pa 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 - PA-IN dataset. It achieves the following results on the evaluation set: - Loss: 1.0443 - Wer: 0.5715 | 3eedbc8c56b6c917f00df3c851f87e92 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | a63b00d818de7e3018182284037f2816 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 4.6694 | 19.22 | 500 | 4.0455 | 1.0 | | 3.3907 | 38.45 | 1000 | 3.2836 | 1.0 | | 2.0866 | 57.67 | 1500 | 1.2788 | ... | 8e79a27d401a7fa5504a9020bbb4624a |
mit | ['torch'] | false | BERT BASE (cased) finetuned on Bulgarian named-entity-recognition data Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This mod... | d91d82ce9f3f7921dbea8e857fd0f253 |
mit | ['torch'] | false | How to use Here is how to use this model in PyTorch: ```python >>> from transformers import pipeline >>> >>> model = pipeline( >>> 'ner', >>> model='rmihaylov/bert-base-ner-theseus-bg', >>> tokenizer='rmihaylov/bert-base-ner-theseus-bg', >>> device=0, >>> revision=None) >>> output = model('Здрав... | 8eb2171daaef6c86678dcc8cd8c9e346 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-base-squad-qg-no-answer` This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asa... | cfa9f87429647ed5c23f0767a4446d2d |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/bart-base-squad-q... | 7ccb99e50414dfd91aec9e58a47f4f20 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-base-squad-qg-no-answer/raw/main/eval/metric.first.sentence.paragraph_sentence.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset ... | 94dae42675a4282731704c46bbf5d342 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-base - max_length: 512 - max_length_output: 32 - epoch: 4 ... | a0e6908349983b3ebd51a1c8e3939c16 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | purple_skin_care Dreambooth model trained by nsaghatelyan 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/fas... | 23ca71f6069940dfefa177b244333648 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-cvbn-37knew 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: 0.2208 - eval_wer: 0.2889 - eval_runtime: 336.8019 - eval_samples_per_second: 8... | 84bf4b2f42eff2e56f87ede53e61ba70 |
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: 5 - mixed_precision_... | 7f8aec1c290b05e139acde505285be05 |
apache-2.0 | ['generated_from_trainer'] | false | bert-small-finetuned-ner-to-multilabel-wnut-17 This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2186 | b5aabcac62d1704bdab94928ec897fd2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1987 | 1.18 | 500 | 0.2114 | | 0.1235 | 2.35 | 1000 | 0.1894 | | 0.0924 | 3.53 | 1500 | 0.1890 | | 0.0735 | 4.71 | 2000 | 0.1958 ... | 071448c770ee1f833f767db367b0fa5e |
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