license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | t5-base-finetuned-qg-context-dataset This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6222 - Rouge1: 36.2283 - Rouge2: 16.0636 - Rougel: 32.6282 - Rougelsum: 32.6551 | dc53e417c8c35217e6bb4ff4c9a05204 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | No log | 1.0 | 73 | 1.8864 | 32.9447 | 13.9495 | 27.5473 | 27.4092 | | No log | 2.0 ... | a395b43adddadf6461b4058f136b93e7 |
apache-2.0 | ['translation', 'wmt16', 'allenai'] | false | Model description This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for en-de. For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369). All 3 models are av... | b31b045db6ca13afab865ac2ea4ae7f3 |
apache-2.0 | ['translation', 'wmt16', 'allenai'] | false | How to use ```python from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "allenai/wmt16-en-de-dist-6-1" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "Machine learning is great, isn't it?" input_ids = tokenizer.encode(inp... | da3a09904e9311bac730ee594a7ceca0 |
apache-2.0 | ['translation', 'wmt16', 'allenai'] | false | Eval results Here are the BLEU scores: model | fairseq | transformers -------|---------|---------- wmt16-en-de-dist-6-1 | 27.4 | 27.11 The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measure... | 764df9562e3f2a7f82e1a8c93bcd830b |
apache-2.0 | ['generated_from_trainer'] | false | Coding challenge The challenge involved building a fake news classifier using the huggingface library. This final model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an fake-and-real-news dataset. The link to the dataset is https://www.kaggle.com/datasets/clme... | 9caec1a27c3a459e22547acc631744da |
apache-2.0 | ['generated_from_trainer'] | false | Training procedure The title and text of each news story was concatenated to form each datapoint. Then a model was finetuned to perform single label classification on each datapoint. The final prediction is the class with the highest probability. | 8467c80fc24664a60cc8a5dd803d73df |
apache-2.0 | ['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 - lr_scheduler_warmup_steps: 500 - num_epochs: 3 | c03b81ba847bb6ef5fe9344a47468c2d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.0503 | 1.0 | 1956 | 0.0025 | 0.9995 | 0.9995 | 0.9995 | 0.9995 | | 0.001 | 2.0 |... | f03289fbddb14ed3f975646f26e9bcb1 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | deployment-with-nvidia-riva) | This model transcribes speech into lowercase Latin alphabet including spaces, and apostroph, and is trained on around 2000 hours of Kinyarwanda speech data. It is a non-autoregressive "large" variant of Conformer, with around 120 million parameters. See the [model architecture]( | f86d3994389e7173c4b88911dd78a7de |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/stt_rw_conformer_ctc_large" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` | 093cac033af8d0d8d2e834200cd93e04 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Training The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/... | 3f8ce36038f96f5b649f628c9313689a |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Performance The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. | Version | Tokenizer | Vocabulary Size | Dev WER| Test WER| Train Dataset | |---------|-----------------... | 3efdc16364417ebc30c7e2ae625aa257 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. The **Stable-Diffusion-v-1-4** checkpoint was initialized with the weights of the [Stable-Diffusion-v-1-2](https://steps/huggingface.co/CompVis/stable-diffusion-v-1-2-original) checkpoint and s... | 9dee02a17d22f1defe411e6fc5653fcd |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Download the weights - [sd-v1-4.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt) - [sd-v1-4-full-ema.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4-full-ema.ckpt) These weights are intended to be used with the original [CompVis S... | c853c3ae19ac820e00ea10e402e856ab |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.... | 8e8acecf63b24c341d70bb94ef302b1f |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other art... | 9d50689c6898dc8d4e0cbba064a8f467 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating envi... | 9e2f2a47a8dca512f4cd5763e2be2c4e |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or p... | 9178e1ed9830a68b5b24627d7be83bfb |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be genera... | 36460280a0d588864b093f970e4bf7e7 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of th... | 07f52aa012dd32b87c8c099a41627785 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling steps show the relative improvements of the checkpoints:  Evaluated using 50 PLMS s... | 3d8134b3254b1f81b91ec078cd30e319 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact | 318ba4a60cf9eca3d37d7368040a9836 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted ... | c3336429e5bc7ce1443e2fc5f716d742 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference... | ef3b13bb776cac7958de8eec745a5c2a |
apache-2.0 | ['romanian', 'seq2seq', 't5'] | false | This is the fine-tuned [mt5-base-romanian](https://huggingface.co/dumitrescustefan/mt5-base-romanian) base model (**390M** parameters). The model was fine-tuned on the [romanian diacritics dataset](https://huggingface.co/datasets/dumitrescustefan/diacritic) for 150k steps with a batch of size 8. The encoder sequence ... | 2cc54f66a4e650ea1fa625cfb550e726 |
apache-2.0 | ['romanian', 'seq2seq', 't5'] | false | How to load the fine-tuned mt5x model ```python from transformers import MT5ForConditionalGeneration, T5Tokenizer model = MT5ForConditionalGeneration.from_pretrained('iliemihai/mt5-base-romanian-diacritics') tokenizer = T5Tokenizer.from_pretrained('iliemihai/mt5-base-romanian-diacritics') input_text = "A inceput sa i... | b8808d49cb11a1ca11c2909dd3efa759 |
mit | ['exbert', 'authorship-identification', 'fire2020', 'pan2020', 'ai-soco'] | false | Model description From scratch pre-trained RoBERTa model with 6 layers and 12 attention heads using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which consists of C++ codes crawled from CodeForces website. | 58479dcbf67303a76005b2d47a640cb4 |
mit | ['exbert', 'authorship-identification', 'fire2020', 'pan2020', 'ai-soco'] | false | Training procedure The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in [`run_language_modelling.py`](https://github.com/huggingface/transformers/blob/master/examples/language... | 6614dbd594e2672e94c22fc87ae41bc6 |
mit | ['exbert', 'authorship-identification', 'fire2020', 'pan2020', 'ai-soco'] | false | BibTeX entry and citation info ```bibtex @inproceedings{ai-soco-2020-fire, title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}", author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and ... | 2ee33bdd183e2e26b59893040f764c91 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_data_aug_rte This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 2.9188 - Accuracy: 0.4874 | 22cc934c043a5da5deed82dc9b9c4f43 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2441 | 1.0 | 1136 | 2.9188 | 0.4874 | | 0.0476 | 2.0 | 2272 | 4.3208 | 0.5054 | | 0.0262 | 3.0 | 3408 | 5.1027 | 0.... | 03e582966d1bb59cbb7fe5ed921e411d |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4690 | bd081e2744b7c8106e77ee4212bc73a1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 18 | 1.5207 | | No log | 2.0 | 36 | 1.5086 | | No log | 3.0 | 54 | 1.4743 | | 3757028dcf84f13e46b1738e7f0856d8 |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased-finetuned-ner This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0778 - Precision: 0.9505 - Recall: 0.9575 - F1: 0.9540 - Accuracy: 0.9886 | 8e378dcf91a512329396d829ce2c6cbd |
apache-2.0 | ['generated_from_trainer'] | false | How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import pipeline from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/bert-large-uncased-finetuned-ner") model = AutoModelForTokenClassification.fr... | cb541e9cbca53c426d0568c5c16240e4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1997 | 1.0 | 878 | 0.0576 | 0.9316 | 0.9257 | 0.9286 | 0.9837 | | 0.04 | 2.0 |... | 9938e3ac946f9beec0de067e498f2a48 |
mit | [] | false | TowerPlace on Stable Diffusion This is the `<TowerPlace>` 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 also... | c8c4b299ac7d254ab0a9ca20454069c5 |
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: 0.2137 - Accuracy: 0.926 - F1: 0.9259 | f33f10e565f8c56e4b7bfb1335de9335 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8322 | 1.0 | 250 | 0.3065 | 0.9105 | 0.9091 | | 0.2386 | 2.0 | 500 | 0.2137 | 0.926 | 0.9259 | | 4088da91468267a2223e77d8b6174d67 |
mit | [] | false | This Repository includes the files required to run the `Predicates Clustering` ORKG-NLP service. Please check [this article](https://orkg-nlp-pypi.readthedocs.io/en/latest/services/services.html) for more details about the service. The [Scikit-Learn](https://scikit-learn.org/stable/) models are converted using [skl2... | 9a35e2dec36635daeab61ffd0943d207 |
mit | [] | false | Vkuoo1 on Stable Diffusion This is the `<style-vkuoo1>` 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 also t... | 22436ab34a5e9151f792ea8dd2312337 |
apache-2.0 | ['automatic-speech-recognition', 'openslr_SLR66', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR66 - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.2680 - Wer: 0.3467 | b4b4cc09abcb024d7e0c213028b28a78 |
apache-2.0 | ['automatic-speech-recognition', 'openslr_SLR66', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | fe4b4a84c2ab89e17555158e8728a75a |
apache-2.0 | ['automatic-speech-recognition', 'openslr_SLR66', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.0304 | 4.81 | 500 | 1.5676 | 1.0554 | | 1.5263 | 9.61 | 1000 | 0.4693 | 0.8023 | | 1.5299 | 14.42 | 1500 | 0.4368 | 0.731... | f0d5407e3885cb97f634dfb3b49ef381 |
creativeml-openrail-m | ['text-to-image'] | false | SksSeisupuSyamuzero Dreambooth model trained by Hirokusa 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... | 024df48469a904700708a23e47760237 |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | `lichenda/Chenda_Li_wsj0_2mix_enh_dprnn_tasnet` This model was trained by LiChenda using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/). Imported from [zenodo](https://zenodo.org/record/4688000). | c41d0d8752d90ff444da6fc0d3902c72 |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 54919e2529d6f58f4550d4a72960f57b83f66dc9 pip install -e . cd egs2/wsj0_2mix/enh1 ./run.sh --skip_data_prep false --skip_train true --download_model lichenda/Chenda_Li_wsj0_2mix_enh_dprnn_tasnet ``` <!-- Generated by ./scripts/utils/show_enh_score.sh --> | 0024042a0601d0d09e1c0062dc74dcf4 |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | Environments - date: `Thu Apr 15 00:03:19 CST 2021` - python version: `3.7.10 (default, Feb 26 2021, 18:47:35) [GCC 7.3.0]` - espnet version: `espnet 0.9.8` - pytorch version: `pytorch 1.5.0` - Git hash: `2aa2f151b5929dc9ffa4df39a8d8c26ca4dbdb85` - Commit date: `Tue Mar 30 09:08:27 2021 +0900` | 00502ce89127f8d06ce74c5fe36b9456 |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | enh_train_enh_dprnn_tasnet_raw config: conf/tuning/train_enh_dprnn_tasnet.yaml |dataset|STOI|SAR|SDR|SIR| |---|---|---|---|---| |enhanced_cv_min_8k|0.960037|19.0476|18.5438|29.1591| |enhanced_tt_min_8k|0.968376|18.8209|18.2925|28.929| | 325ea1e78f3cc28b7d10464c689e1f03 |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | ENH config <details><summary>expand</summary> ``` config: conf/tuning/train_enh_dprnn_tasnet.yaml print_config: false log_level: INFO dry_run: false iterator_type: chunk output_dir: exp/enh_train_enh_dprnn_tasnet_raw ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_worl... | 4a44765e1c895233c0d17b5e170585f6 |
gpl-3.0 | [] | false | Model description
DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
with no humans labelling them in any way (which is why it can use lo... | ec8dde4bf8e5fe6d57c038a84929cf50 |
gpl-3.0 | [] | false | Intended uses & limitations
This can only be used for the kind of news that are similar to the ones in the dataset,
please visit the [dataset's kaggle page](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) to see the data.
| 180c0d61f27421386770dfff2c2c290a |
gpl-3.0 | [] | false | How to use
You can use this model directly with a :
```python
>>> from transformers import pipeline
>>> classifier = pipeline("text-classification", model="Giyaseddin/distilbert-base-cased-finetuned-fake-and-real-news-dataset", return_all_scores=True)
>>> examples = ["Yesterday, Speaker Paul Ryan tweeted a vi... | cf01aca15a6bf3bbfcbf4d7f6ef3c51a |
gpl-3.0 | [] | false | 1 campaign promise of President Donald Trump, the border wall.Here s the rub.Here s what pundits never discuss.The Republican party doesn t need a single Democrat to fund the border wall.A single spending bill could come from the House of Representatives that fully funds 100% of the border wall. The spending bill then ... | 927658169a786233dfabd6722bbb8372 |
gpl-3.0 | [] | false | Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions. It also inherits some of
[the bias of its teacher model](https://huggingface.co/bert-base-uncased | c954b7ead62e1a3c2028d787a81e7461 |
gpl-3.0 | [] | false | Pre-training data
DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset
consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia)
(excluding lists, tables and headers).
| 306a5edc77e07ce43deaa686d611c732 |
gpl-3.0 | [] | false | Preprocessing
In the preprocessing phase, both the title and the text of the news are concatenated using a separator `[SEP]`.
This makes the full text as:
```
[CLS] Title Sentence [SEP] News text body [SEP]
```
The data are splitted according to the following ratio:
- Training set 60%.
- Validation set 2... | fab9ed25ecb3af93c77d449b472b21a1 |
gpl-3.0 | [] | false | Fine-tuning
The model was finetuned on GeForce GTX 960M for 5 hours. The parameters are:
| Parameter | Value |
|:-------------------:|:-----:|
| Learning rate | 5e-5 |
| Weight decay | 0.01 |
| Training batch size | 4 |
| Epochs | 3 |
Here is the scores ... | 697e8bab330534efdba73e996e4905f7 |
gpl-3.0 | [] | false | Evaluation results
When fine-tuned on downstream task of fake news binary classification, this model achieved the following results:
(scores are rounded to 2 floating points)
| | precision | recall | f1-score | support |
|:------------:|:---------:|:------:|:--------:|:-------:|
| Fake | ... | 01388937d3b2bfec7734f2eab3f99c20 |
mit | ['pytorch', 'diffusers', 'unconditional-image-generation', 'diffusion-models-class'] | false | Model Card for Stable Diffusion - Pokemon, 256px Model developed for the Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class). This model is a diffusion model for unconditional image generation of Pokemon [](h... | 690e90044910e506467cd41b1f87aef0 |
mit | [] | false | liliana-vess on Stable Diffusion This is the `<liliana-vess>` 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 ... | 8055840ae080d452b98c729e8894087f |
mit | [] | false | angus mcbride style on Stable Diffusion This is the `<angus-mcbride-style>` 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) note... | 3c57e7387702f80f6db432c633f7e6f7 |
cc | [] | false | WELCOME TO MY FIRST REPO Making fictional characters into Stable Diffusion models M3GAN Go to files and versions and download any of the .ckpt models. Drop them into your local installation of Stable Diffusion and enjoy! NOTE: all of these models (except v5, v6 and v7 (diff-mini)) have been trained on SD-1.5, so th... | 06e5c9746996623a49b2bb74fd6b3c5c |
apache-2.0 | ['finnish', 'electra'] | false | ELECTRA for Finnish Pretrained ELECTRA model on Finnish language using a replaced token detection (RTD) objective. ELECTRA was introduced in [this paper](https://openreview.net/pdf?id=r1xMH1BtvB) and first released at [this page](https://github.com/google-research/electra). **Note**: this model is the ELECTRA discri... | d1f30ffb7a0121ff6abb0535c6bf16ed |
apache-2.0 | ['finnish', 'electra'] | false | Model description Finnish ELECTRA is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to ... | c2387f1eec9d1afbd21f4e14a7d30bbc |
apache-2.0 | ['finnish', 'electra'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import ElectraTokenizer, ElectraModel import torch tokenizer = ElectraTokenizer.from_pretrained("Finnish-NLP/electra-base-discriminator-finnish") model = ElectraModel.from_pretrained("Finnish-NLP/ele... | d821e16d8ab3f897a6b37652d1e98442 |
apache-2.0 | ['finnish', 'electra'] | false | Training data This Finnish ELECTRA model was pretrained on the combination of five datasets: - [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and... | 4f64494343f11a8bda07281b43444cae |
apache-2.0 | ['finnish', 'electra'] | false | Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 1M steps. The optimizer used was a AdamW with learning rate 2e-4, learning rate warmup for 20000 steps and linear decay of the learning rate after. Training code was from the o... | 5de47493564b1b4c7cd1afd8b23b8f9d |
apache-2.0 | ['finnish', 'electra'] | false | Evaluation results Evaluation was done by fine-tuning the model on downstream text classification task with two different labeled datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Yle News classification fine-tuning was done with two different sequ... | 51f7b68b9e4e61a08a9b0f8e592131b7 |
apache-2.0 | ['translation'] | false | eng-vie * source group: English * target group: Vietnamese * OPUS readme: [eng-vie](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-vie/README.md) * model: transformer-align * source language(s): eng * target language(s): vie vie_Hani * model: transformer-align * pre-processing: normaliz... | 59135063b078df5f621c7fd670ff8fee |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-vie - source_languages: eng - target_languages: vie - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-vie/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'vi'] - src_constituents: {'eng'} - tgt_const... | b58338d212ab73d5774530dc72f56b18 |
apache-2.0 | [] | false | distilbert-base-uk-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 accuracy... | e3b9e7ac36cedd7a998966f8fdc04099 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-uk-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-uk-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github r... | 71bf3cff26e20725f029f93931d4cd94 |
creativeml-openrail-m | ['Lora', 'stable-diffusion'] | false | Introduction of LoraByTanger - 介绍 欢迎使用由Tanger训练的Lora模型，此模型库包括了本人当前训练并筛选出的所有人物Lora模型，目前以原神角色为主，但未来也会包括更多的游戏人物与二次元角色（一切以个人喜好与XP为准）。 Welcome to LoraByTanger. This model library includes all the Lora models I have trained and selected so far. Currently, the GenShin Impact characters are main, but in the future, more ... | dc91fcaab57ef53018b003e8988697c9 |
creativeml-openrail-m | ['Lora', 'stable-diffusion'] | false | Composition - 组成 每个Lora文件夹包含了-Each Lora folder contains: 1. Lora Model 2. test.png (Generated by ["AbyssOrangeMix2_hard.safetensors"](https://huggingface.co/WarriorMama777/OrangeMixs/tree/main/Models/AbyssOrangeMix2)) 3. Good pics * n (Generated by other models) 4. "xx配置文件.json" means the training parameters... | cacbe29512f3dfe9e2ac33a1c86383db |
creativeml-openrail-m | ['Lora', 'stable-diffusion'] | false | Suggestions - 建议 适当降低权重使用可确保一定的泛化能力（换衣服），如果出现某个原配服饰无法消除，可在负面词会添加对应tag~ 适当调高cfg有益于提升画面精细程度，且不同模型有不同的适宜区间。 Appropriately reduce the use of weight can ensure a certain generalization ability (change clothes). If there is a certain original clothing can not be eliminated, please add the corresponding tag to the ne... | 45095740bdf9c7032ca09982e475c024 |
creativeml-openrail-m | ['Lora', 'stable-diffusion'] | false | Examples - 例图 所有测试图均由["AbyssOrangeMix2_hard"](https://huggingface.co/WarriorMama777/OrangeMixs/tree/main/Models/AbyssOrangeMix2)生成，至于为什么全是死库水，可能<del>（这就是爱）</del>我只是想测试一下Lora的泛化能力。如果想要完全还原角色，建议随便找一张角色原图反推tag后，加入prompt以还原角色，或者干脆不额外使用服装等影响角色形象的tag。 (可在文件列表中找到原图，并放入WebUi查看关键词等信息) - (You can find the original image ... | e35433246760617434dc51f05f5dd9cf |
creativeml-openrail-m | ['Lora', 'stable-diffusion'] | false | Mobius_梅比乌斯 所有这些都包括默认皮肤和其他两种皮肤。V7更容易重现角色，但泛化较差，使用只需输入模型名里的tag即可。V9具有更好的泛化性，但不太容易复刻角色，需要更多tag辅助，还需要一定的运气。 建议直接抄我上传的图像里的tags。 All include default skin and other two skins. V7 is easier to reproduce but poor generalization, just use the tags in name. V9 has better generalization but not very easy to rep... | 254d3d700287610625d1abdea404f8e5 |
creativeml-openrail-m | ['Lora', 'stable-diffusion'] | false | (v9)mobius_mobius-w_mobius-x_mobius-o-000006 <img src=https://huggingface.co/Tanger/LoraByTanger/resolve/main/char/Mobius_梅比乌斯/(v9)mobius_mobius-w_mobius-x_mobius-o-000006.png width="300" height=""> more <div><img src="https://huggingface.co/Tanger/LoraByTanger/resolve/main/char/Ranni_菈妮/1.png"></div> <di... | 3f115a1edf858ac53319468eb4a1bbf6 |
apache-2.0 | [] | false | bert-base-en-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... | 5685f43da11a2aec11f4c307a0c43322 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https:/... | 944e036d7d6f03526d6a632c2cec2efd |
apache-2.0 | ['speech-recognition', 'librispeech_asr', 'generated_from_trainer'] | false | hubert-librispeech-clean-100h-demo-dist This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) on the LIBRISPEECH_ASR - CLEAN dataset. It achieves the following results on the evaluation set: - Loss: 0.0984 - Wer: 0.0883 | 0103469fe942a5dd1d1d7a76a0da3e7d |
apache-2.0 | ['speech-recognition', 'librispeech_asr', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | 9f406d3596d96346457f1cf6ad85bd8f |
apache-2.0 | ['speech-recognition', 'librispeech_asr', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.9031 | 0.11 | 100 | 2.9220 | 1.0 | | 2.6437 | 0.22 | 200 | 2.6268 | 1.0 | | 0.3934 | 0.34 | 300 | 0.4860 | 0.4182 | |... | a5cc8e507b1f560de8649ff92974c985 |
creativeml-openrail-m | [] | false | Prompt with **"nanachiDB cute furry girl"** **Training details (as far as I remember):** - Trained with [JoePenna Dreambooth repository](https://github.com/JoePenna/Dreambooth-Stable-Diffusion) - data set: 42 concept images + a number of custom reg images - default learning rate for 5000 steps - trained on top of yi... | c8b5c2bb801f119aac8df5448b395ac6 |
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: 115 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 20 | 030c71b77d7a5013e05b5054a44f4e91 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | CRDNN with CTC/Attention trained on CommonVoice 7.0 German (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on CommonVoice (German Language) within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrai... | 76b96cc3fcea93097f84afb0846d5985 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and trained with the train transcriptions (train.tsv) of CommonVoice (DE). - Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of N blocks of convoluti... | 60af92abb49b67ebe9bec00ef3c8165f |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Install SpeechBrain First of all, please install SpeechBrain with the following command: ``` pip install speechbrain ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). | a6e7cb01b50748211717d5e89d200ec3 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Transcribing your own audio files (in German) ```python from speechbrain.pretrained import EncoderDecoderASR asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-commonvoice-de", savedir="pretrained_models/asr-crdnn-commonvoice-de") asr_model.transcribe_file("speechbrain/asr-crdnn-commonvoice-de/e... | 88e6c17386a6f818ef25d339a0555d17 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Parallel Inference on a Batch Please, [see this Colab notebook](https://colab.research.google.com/drive/1hX5ZI9S4jHIjahFCZnhwwQmFoGAi3tmu?usp=sharing) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model. | 31383359952ec8da97ed07aa147b92d4 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Training The model was trained with SpeechBrain (986a2175). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes... | a9c6bbca802bb57901c3a0352d43839b |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | **Citing SpeechBrain** Please, cite SpeechBrain if you use it for your research or business. ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan ... | a9469d33a6768068dd6fd83e7acb77d5 |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased-finetuned-youcook_4 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: 1.9929 | be57f7767b590d8fda797e5027d398f5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 5 - eval_batch_size: 5 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_traini... | 1f040f3433cd655acb45e71b1b5795f4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.3915 | 1.0 | 206 | 2.1036 | | 2.0412 | 2.0 | 412 | 2.2207 | | 1.9062 | 3.0 | 618 | 1.7281 | | cd71b0701f84c1864387d1fa2c7359a1 |
apache-2.0 | ['Vocoder', 'HiFIGAN', 'text-to-speech', 'TTS', 'speech-synthesis', 'speechbrain'] | false | Vocoder with HiFIGAN trained on LibriTTS This repository provides all the necessary tools for using a [HiFIGAN](https://arxiv.org/abs/2010.05646) vocoder trained with [LibriTTS](https://www.openslr.org/60/) (with multiple speakers). The sample rate used for the vocoder is 16000 Hz. The pre-trained model takes in inp... | 13fd6c618f07c1c7740eb560b6463d42 |
apache-2.0 | ['Vocoder', 'HiFIGAN', 'text-to-speech', 'TTS', 'speech-synthesis', 'speechbrain'] | false | Using the Vocoder ```python import torch from speechbrain.pretrained import HIFIGAN hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-libritts-16kHz", savedir="tmpdir") mel_specs = torch.rand(2, 80,298) | d52a639974eaa1279ae15d331dd8223b |
apache-2.0 | ['Vocoder', 'HiFIGAN', 'text-to-speech', 'TTS', 'speech-synthesis', 'speechbrain'] | false | Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/LibriTTS/v... | c1641c225ecb993d9cd3142379162189 |
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