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 | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 4.55 | 200 | 2.9217 | 0.9846 | | No log | 9.09 | 400 | 1.2293 | 0.7093 | | 2.3111 | 13.64 | 600 | 0.3885 | 0.3602 | |... | 8186479a4fb25c0eec725123d8543e7b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Vietnamese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Vietnamese using the [Common Voice](https://huggingface.co/datasets/common_voice), [Infore_25h dataset](https://files.huylenguyen.com/25hours.zip) (Password: BroughtToYouByInfoRe)... | 32158f984036984723d5de7ea58b419c |
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", "vi", split="test[:2%]") processor = Wav2Vec2Processor.from_p... | fa4ad5c9449f39ed22059b8ba2769d47 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2]... | f529b1bc488654457e8e7ec44dd43782 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Vietnamese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "vi", split="test")... | 78c9df8a405ea119c538211f899dd914 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits,... | 6de2b0b4424dee81b87c4f1d0ddf6dd2 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train`, `validation`, and `Infore_25h` datasets were used for training The script used for training can be found [here](https://drive.google.com/file/d/1AW9R8IlsapiSGh9n3aECf23t-zhk3wUh/view?usp=sharing) =======================To here===============================> Your model in then ... | 2fc16a2213fe0796733daf7ddb19a30e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | How to evaluate my trained checkpoint Having uploaded your model, you should now evaluate your model in a final step. This should be as simple as copying the evaluation code of your model card into a python script and running it. Make sure to note the final result on the model card **both** under the YAML tags at t... | 18ade77b6fea1ed98afdfc3a5263d558 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Rules of training and evaluation In this section, we will quickly go over what data is allowed to be used as training data, what kind of data preprocessing is allowed be used, and how the model should be evaluated. To make it very simple regarding the first point: **All data except the official common voice `test` ... | c46ece7916458f0ab5f920caf6971f1c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Further reading material It is recommended that take some time to read up on how Wav2vec2 works in theory. Getting a better understanding of the theory and the inner mechanisms of the model often helps when fine-tuning the model. **However**, if you don't like reading blog posts/papers, don't worry - it is by no m... | 93db112f80aa14496f995e72db01ad6d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | FAQ - Can a participant fine-tune models for more than one language? Yes! A participant can fine-tune models in as many languages she/he likes - Can a participant use extra data (apart from the common voice data)? Yes! All data except the official common voice `test data` can be used for training. If a participant w... | bcba26e903921dd81b629b9c0cc5faa6 |
mit | ['generated_from_trainer'] | false | gpt2.CEBaB_confounding.food_service_positive.sa.5-class.seed_43 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.9481 - Accuracy: 0.5649 - Macro-f1: 0.5174 - Weighted-macro-f1: 0.5326 | 14f486419ca2e58c8e07cb6a293af98a |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Norwegian-Nynorsk | Feature | Description | | --- | --- | | **Name** | `nb_udv25_norwegiannynorsk_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `exper... | 0eb69e1cb304b86cb4c6bb9d51eeaa0a |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (1400 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`,... | 5509866b5e222f1cc94fec2c3165e688 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.96 | | `TOKEN_P` | 99.96 | | `TOKEN_R` | 99.96 | | `TOKEN_ACC` | 99.99 | | `SENTS_F` | 99.10 | | `SENTS_P` | 99.15 | | `SENTS_R` | 99.05 | | `TAG_ACC` | 98.33 | | `POS_ACC` | 98.34 | | `MORPH_ACC` | 97.91 | | `DEP_UAS` | 94.11 | | `DEP_LAS` | 92.14 | | `LEMMA_A... | d6bd953042b27e6e255ed738d0449e1f |
creativeml-openrail-m | ['text-to-image'] | false | Duskfall's Final of Fantasea Pt 3 Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/g... | a3f9eba9c0afe875c5e2657eacd11f5f |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples 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: 0.3105 - Accuracy: 0.8667 - F1: 0.8667 | b1095e7559ae5e848a8986892bba2e28 |
afl-3.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer', 'hf-asr-leaderboard'] | false | wav2vec 2.0 with CTC trained on CommonVoice Spanish (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on CommonVoice (Spanish Language) within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](ht... | eeb93524065658f1a6e827872905e944 |
afl-3.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer', 'hf-asr-leaderboard'] | false | Transcribing your own audio files (in Spanish) ```python from speechbrain.pretrained import EncoderASR asr_model = EncoderASR.from_hparams(source="Voyager1/asr-wav2vec2-commonvoice-es", savedir="pretrained_models/asr-wav2vec2-commonvoice-es") asr_model.transcribe_file("Voyager1/asr-wav2vec2-commonvoice-es/example-es... | becd6fad73ca61a92c01959bd4c52920 |
afl-3.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer', 'hf-asr-leaderboard'] | false | **Citations** ```bibtex @article{lopez2022tid, title={TID Spanish ASR system for the Albayzin 2022 Speech-to-Text Transcription Challenge}, author={L{\'o}pez, Fernando and Luque, Jordi}, journal={Proc. IberSPEECH 2022}, pages={271--275}, year={2022} } @misc{https://doi.org/10.48550/arxiv.2210.15226, doi... | f82b7f65f6c336e397537fb9726160fe |
apache-2.0 | ['xlm-roberta-large', 'semantic role labeling', 'finetuned', 'dependency parsing'] | false | Model description
This model is the [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) fine-tuned first on the Universal Dependencies Portuguese dataset, then fine-tuned on the CoNLL formatted OntoNotes v5.0 and then fine-tuned on the PropBank.Br data. This is part of a project from which resulted the ... | b0b63628a989bb93f648dd16fecf0811 |
apache-2.0 | ['xlm-roberta-large', 'semantic role labeling', 'finetuned', 'dependency parsing'] | false | How to use
To use the transformers portion of this model:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("liaad/ud_srl-enpt_xlmr-large")
model = AutoModel.from_pretrained("liaad/ud_srl-enpt_xlmr-large")
```
To use the full SRL model (transformers port... | 8ffabf27b8b9812f848864610aab5d4c |
apache-2.0 | ['xlm-roberta-large', 'semantic role labeling', 'finetuned', 'dependency parsing'] | false | Limitations and bias
- This model does not include a Tensorflow version. This is because the "type_vocab_size" in this model was changed (from 1 to 2) and, therefore, it cannot be easily converted to Tensorflow.
- The model was trained only for 10 epochs in the Universal Dependencies dataset.
- The model was trai... | c9cb95252937ad54e94fcc16feefda92 |
apache-2.0 | ['xlm-roberta-large', 'semantic role labeling', 'finetuned', 'dependency parsing'] | false | Training procedure
The model was trained on the Universal Dependencies Portuguese dataset; then on the CoNLL formatted OntoNotes v5.0; then on Portuguese semantic role labeling data (PropBank.Br) using 10-fold Cross-Validation. The 10 resulting models were tested on the folds as well as on a smaller opinion dataset... | 53fad5581d6e614ca738430b3d4187f6 |
mit | ['generated_from_trainer'] | false | fervent_benz 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-chunk3-250000... | 40412f2c589d0e8aec126bd96dd6ed27 |
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... | 7bcb40aea975f90050fdf3c696a8bb5a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 7231c3043f4951c76fb6b01a18209be6 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_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: 0.6927 - Accuracy: 0.5271 | 86bd6381abaa1cf740e069c2e0e3019f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6943 | 1.0 | 20 | 0.6933 | 0.4765 | | 0.6944 | 2.0 | 40 | 0.6927 | 0.5271 | | 0.6932 | 3.0 | 60 | 0.6929 | 0.... | 44b17c288ab044cad494887da2ce2882 |
apache-2.0 | ['generated_from_keras_callback'] | false | nbalepur/distilbert-base-uncased-finetuned-imdb 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: 2.0308 - Epoch: 0 | f365074d1005681d2de31c105acc70de |
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... | 581d8d32f3e190923241c4f400a388ec |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precisio... | 037546c078e84daeedb069e492ed2611 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-multilingual-cased-finetuned-squad-squadv This model is a fine-tuned version of [monakth/bert-base-multilingual-cased-finetuned-squad](https://huggingface.co/monakth/bert-base-multilingual-cased-finetuned-squad) on the squad_v2 dataset. | 96ca8049ff37afe14b00a966c812846d |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | diffusionAI Dreambooth model trained by aaronsiim with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get starte... | 63b21901fa60105ac88a1397d82cfffb |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-switchboard-2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on Switchboard dataset. It achieves the following results on the validation set: - Loss: 0.7090 - Accuracy: 0.7215 - Precision: 0.7176 - Recall: 0.7215 - F1: ... | bf1ac2842d317c856d62b623356ef77f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.2139 | 1.0 | 370 | 0.8510 | 0.6875 | 0.6831 | 0.6875 | 0.6846 | | 0.3195 | 2.0 |... | 8b612b560c8b82ad0027051b646c740a |
cc-by-4.0 | ['answer extraction'] | false | Model Card of `lmqg/mbart-large-cc25-dequad-ae` This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for answer extraction on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (dataset_name: default) via [`lmqg`](https://github.com/asahi41... | d97116e55e823c62fc28cfba794d2759 |
cc-by-4.0 | ['answer extraction'] | false | Overview - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) - **Language:** de - **Training data:** [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://g... | c99ce52187de31cfa65fa3976b3227ef |
cc-by-4.0 | ['answer extraction'] | false | model prediction answers = model.generate_a("das erste weltweit errichtete Hermann Brehmer 1855 im niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-dequad-ae") output =... | 4e19261d6f837c37add979653ad8d767 |
cc-by-4.0 | ['answer extraction'] | false | Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-dequad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_dequad.default.json) | | Score | Type | Dataset ... | 75e4589dd6600e986e34fcfc357e2267 |
cc-by-4.0 | ['answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_dequad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: None - model: facebook/mbart-large-cc25 - max_length: 512 - max_length_output: 32 - epoc... | 176ed6fe9cef4f93046a39dca036d1f3 |
mit | [] | false | This model was trained on a new dataset composed of available poems by Anne Bradstreet hosted by [Public Domain Poetry.](https://www.public-domain-poetry.com/anne-bradstreet) Specifically I downloaded all 40 poems and fine-tuned a bert-base-uncased text classification model on Amazon SageMaker. For the negative class, ... | 5a54a6bf60528667f3abc378d6a28492 |
apache-2.0 | ['generated_from_trainer'] | false | ⚙️ Model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [Emotion Dataset from Kaggle](https://www.kaggle.com/datasets/praveengovi/emotions-dataset-for-nlp). It achieves the following results on the test set after being trained and evaluated with the Trainer i... | c76a6df71f19cc4bf4a5612fbbfe4600 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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 | c55f69cf4c51a5b3b526a392bafbf944 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 1.5691 | 1.0 | 250 | 1.2681 | 0.564 | 0.4477 | 0.3868 | 0.564 | | 0.9132 | 2.0 |... | ba50b3d1e1763669ca3a576190914ab7 |
gpl-3.0 | ['spacy', 'token-classification'] | false | Introduction spaCy NER model for Spanish trained with interviews in the domain of tourism related to the Way of Saint Jacques. It recognizes four types of entities: location (LOC), organizations (ORG), person (PER) and miscellaneous (MISC). | Feature | Description | | --- | --- | | **Name** | `es_spacy_ner_cds` | | ... | f6f2c622ffe2702bcede3555062faf58 |
gpl-3.0 | ['spacy', 'token-classification'] | false | Usage You can use this model with the spaCy *pipeline* for NER. ```python import spacy from spacy.pipeline import merge_entities nlp = spacy.load("es_spacy_ner_cds") nlp.add_pipe('sentencizer') example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. El proyecto lo financia el Ministerio de Industria ... | f1d84cf3cac30cd3230339c3bcd713a7 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the coco concept trained by avocadogogo. This is a Stable Diffusion model fine-tuned on the coco concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of coco cat** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https:... | 44bee8a3753dec639952dec9085483ef |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | cases（prompt） a photo of coco cat sitting on top of the deck of a battle ship traveling through the open sea with a lot of ships surrounding it  a photo of coco cat wearing awesome glasses in a forest fu... | ee8b5e67d0b4bd83a3992b52e55c91c2 |
apache-2.0 | ['vision', 'image-classification'] | false | LeViT LeViT-128S model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference ](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT). Dis... | 526006e1a6e4e7f905e073c0348fac28 |
apache-2.0 | ['vision', 'image-classification'] | false | Usage Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import LevitFeatureExtractor, LevitForImageClassificationWithTeacher from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.j... | eb8d8968646cdc195d05a7e19c2a9206 |
mit | [] | false | Wayne Reynolds Character on Stable Diffusion This is the `<warcharport>` 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) noteboo... | 382a7a981e634338ef9937e733405089 |
apache-2.0 | ['translation'] | false | opus-mt-ja-fi * source languages: ja * target languages: fi * OPUS readme: [ja-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ja-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | 51b34dea6db97076c4342291e0475da4 |
apache-2.0 | ['speech-recognition', 'common_voice', 'generated_from_trainer'] | false | wav2vec2-common_voice-ab-demo This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - AB dataset. It achieves the following results on the evaluation set: - Loss: 15.1812 - Wer: 1.0 | 87509bdd2c5e7d6e30ffa2622efc4bec |
apache-2.0 | ['speech-recognition', 'common_voice', '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... | 448f2bb2ef5b1d38f76ebda770014bcc |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | En-Zu_update This model is a fine-tuned version of [kabelomalapane/test_model1.2_updated](https://huggingface.co/kabelomalapane/test_model1.2_updated) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7101 - Bleu: 11.8551 | 196bf7c2c8f22b4acd5b11cc387137da |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | f7f01d1729e10bee55d797b69a04f6bb |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 1.9111 | 1.0 | 1173 | 1.7594 | 11.7012 | | 1.7191 | 2.0 | 2346 | 1.7279 | 12.0250 | | 1.5709 | 3.0 | 3519 | 1.7172 | 1... | c0807950f05aeb750a38b44c3775f693 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-300m-demo-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 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.9475 - eval_wer: 1.0377 - eval_runtime: 70.5646 - eval_samples_p... | 373e935f9fe18a90433f7dee3e68ccc7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 300 - mixed_precision_t... | 689a4c9760d6cade906302e9a3ece20f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-fi-en Neural machine translation model for translating from Finnish (fi) to English (en). 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 world. All model... | a8e75c4eff62cac391d4daa4edc40d0f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2021-12-08 * source language(s): fin * target language(s): eng * model: transformer (big) * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt-2021-12-08.zip](https://object.po... | 77c17b62a2c91318ee8c71814e3e6154 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Kolme kolmanteen on kaksikymmentäseitsemän.", "Heille syntyi poikavauva." ] model_name = "pytorch-models/opus-mt-tc-big-fi-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMT... | 28df591c752bf5724d171406d8893baa |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt-2021-12-08.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opusTCv20210807+bt-2021-12-08.test.txt) * test set scores: [opusTCv20210807+bt-2021-12-08.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opusTCv20210807+bt-2021-12-08.eva... | 4a0da541d76dcc1e7b90607e19029a4e |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | fin-eng | tatoeba-test-v2021-08-07 | 0.72298 | 57.4 | 10690 | 80552 | | fin-eng | flores101-devtest | 0.62521 | 35.4 | 1012 | 24721 | | fin-eng | newsdev2015 | 0.56232 | 28.6 | 1500 | 32012 | | fin-eng | newstest2015 | 0.57469 | 29.9 | 1370 | 27270 | | f... | 3863a901b71be635c4f36bfd0e2249c1 |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3322 - Accuracy: 0.9279 | 50a4257a3cc774bed3c1e7013ac86e8a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 0.3177 | 1.0 | 152679 | 0.3123 | 0.9248 | | 0.2212 | 2.0 | 305358 | 0.3322 | 0.9279 | | f922b765eb27929c71ccb63c63d2fe6a |
mit | ['generated_from_trainer'] | false | tluo_xml_roberta_base_amazon_review_sentiment_v4 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: 0.9589 - Accuracy: 0.6137 | e4b8792b41cf4a763be450f623411d36 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.5745609276104923e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 25 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ... | 0ac99a4cb7441e00f7846a3a4fdeeb19 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.1074 | 0.17 | 5000 | 1.0468 | 0.5493 | | 1.0461 | 0.33 | 10000 | 1.0222 | 0.558 | | 1.0245 | 0.5 | 15000 | 0.9776 ... | 98b4b6a33207bc25a400d9cee61cd085 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-multilingual-cased-finetuned-cola This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 0.1729 - Accuracy: 0.9755 | 4fa416d82d027041e96543782736d94b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5119 | 1.0 | 625 | 0.2386 | 0.922 | | 0.2536 | 2.0 | 1250 | 0.2055 | 0.949 | | 0.1718 | 3.0 | 1875 | 0.1733 | 0.... | 2ed0020db2ec7d75bbea3ed7b363d3da |
mit | ['malaysian-distilbert-small'] | false | Malaysian DistilBERT Small Malaysian DistilBERT Small is a masked language model based on the [DistilBERT model](https://arxiv.org/abs/1910.01108). It was trained on the [OSCAR](https://huggingface.co/datasets/oscar) dataset, specifically the `unshuffled_original_ms` subset. The model was originally HuggingFace's pre... | b39a143ca20576dbfe54fc5f3a0bbc28 |
mit | ['malaysian-distilbert-small'] | false | params | Arch. | Training/Validation data (text) | |------------------------------|---------|------------------|----------------------------------------| | `malaysian-distilbert-small` | 66M | DistilBERT Small | OSCAR `unshuffled_original_ms` Dataset | | 8a2dc7dde468d49ab71d8a4229dde076 |
mit | ['malaysian-distilbert-small'] | false | Evaluation Results The model was trained for 1 epoch and the following is the final result once the training ended. | train loss | valid loss | perplexity | total time | |------------|------------|------------|------------| | 2.476 | 2.336 | 10.33 | 0:40:05 | | fd72d551f18189fdc12652d7f022ce85 |
mit | ['malaysian-distilbert-small'] | false | As Masked Language Model ```python from transformers import pipeline pretrained_name = "w11wo/malaysian-distilbert-small" fill_mask = pipeline( "fill-mask", model=pretrained_name, tokenizer=pretrained_name ) fill_mask("Henry adalah seorang lelaki yang tinggal di [MASK].") ``` | 60eec96c29dc02377acdee324e284b54 |
mit | ['malaysian-distilbert-small'] | false | Feature Extraction in PyTorch ```python from transformers import DistilBertModel, DistilBertTokenizerFast pretrained_name = "w11wo/malaysian-distilbert-small" model = DistilBertModel.from_pretrained(pretrained_name) tokenizer = DistilBertTokenizerFast.from_pretrained(pretrained_name) prompt = "Bolehkah anda [MASK] B... | e0c1949ecf473f6cede9005aa6e2b846 |
apache-2.0 | ['classical chinese', 'literary chinese', 'ancient chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a BERT model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing, derived from [bert-ancient-chinese](https://huggingface.co/Jihuai/bert-ancient-chinese). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech) and [FEATS]... | aed62793c93c464e051345f81b218b8d |
apache-2.0 | ['classical chinese', 'literary chinese', 'ancient chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-ancient-chinese-base-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-ancient-chinese-base-upos") ``` or ```py import esupar nlp=esupar.... | 3aa5f566d1d3b15cb9312d2149340b90 |
gpl-3.0 | ['generated_from_trainer'] | false | gpt2-base-chinese-finetuned-job-resume This model is a fine-tuned version of [ckiplab/gpt2-base-chinese](https://huggingface.co/ckiplab/gpt2-base-chinese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2658 | eeb64239e11da1458f8bcf278afac050 |
gpl-3.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 480 | 2.3271 | | 2.4967 | 2.0 | 960 | 2.2729 | | 2.2259 | 3.0 | 1440 | 2.2658 | | 46dc0b0113ed36eef0fbd1e07eeb4c72 |
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: 0.2185 - Accuracy: 0.928 - F1: 0.9281 | c0cd37b05b4646f663585f3043331984 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8374 | 1.0 | 250 | 0.3188 | 0.9045 | 0.9012 | | 0.254 | 2.0 | 500 | 0.2185 | 0.928 | 0.9281 | | 0cd76bdab11a0640ab1a4dae63590970 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-vanilla-cstop_artificial This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1506 - Exact Match: 0.5725 | 1302336a3e518022d663c0d3eeae5dc3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | |:-------------:|:------:|:----:|:---------------:|:-----------:| | 1.4041 | 28.5 | 200 | 0.1008 | 0.4758 | | 0.047 | 57.13 | 400 | 0.1029 | 0.5367 | | 0.021 | 85.63 | 600 | 0.1... | 0f9542fd43eb5c6fca6b763ef03ac95c |
other | ['computer_vision', 'pose_estimation'] | false | Model contributed by Claire Witham at Centre for Macaques, MRC Harwell, UK. This model is trained on photos and videos of rhesus macaque faces – mostly forward facing or in profile. Includes range of ages from infant to adult and both sexes. Shows reasonable transference to other primates especially other macaque spec... | 24654c190d3a30a50e23694bea3ecdd5 |
mit | ['spacy', 'token-classification'] | false | ru_core_news_md Russian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer. | Feature | Description | | --- | --- | | **Name** | `ru_core_news_md` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `morphologiz... | 0b083d67a930bf4ea5a05f7525105f57 |
mit | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.68 | | `TOKEN_P` | 97.28 | | `TOKEN_R` | 98.31 | | `TOKEN_F` | 97.79 | | `POS_ACC` | 98.82 | | `MORPH_ACC` | 97.29 | | `MORPH_MICRO_P` | 98.88 | | `MORPH_MICRO_R` | 98.17 | | `MORPH_MICRO_F` | 98.52 | | `SENTS_P` | 99.87 | | `SENTS_R` | 99.85 | | `SENTS_F` | ... | 46d752eeac2146105078eb369b935ce9 |
mit | [] | false | Iberê Thenório on Stable Diffusion This is the `<ibere-thenorio>` 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 ... | 8b0195e7164aaef81eea934ba448567a |
apache-2.0 | ['generated_from_trainer'] | false | tiny-vanilla-target-tweet This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9887 - Accuracy: 0.7032 - F1: 0.7042 | 3054f4c7a0d76ad3696eb9c8da60b720 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.1604 | 4.9 | 500 | 0.9784 | 0.6604 | 0.6290 | | 0.7656 | 9.8 | 1000 | 0.8273 | 0.7139 | 0.6905 | | 0.534 |... | ebcbd06b17179de602f0f886f46a499a |
apache-2.0 | ['generated_from_trainer'] | false | hate_speech_detection_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0923 - Accuracy: 0.97 - F1: 0.9698 | d97a90ded64341828fa2886cf022c461 |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2090 - Accuracy: 0.9235 - F1: 0.9237 | 2287486393fb670c224777faaf32eaa0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8154 | 1.0 | 250 | 0.3031 | 0.909 | 0.9063 | | 0.2428 | 2.0 | 500 | 0.2090 | 0.9235 | 0.9237 | | 20cdfd11f62908d6885c7d1de923954f |
mit | ['generated_from_trainer'] | false | bertimbau-base-finetuned-brazilian_court_decisions_bt8_ep15 This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.814463 - Accuracy: 0.777228 ... | 23ac646adf74095739f4c6f9f16a8f23 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 | ab6de13e98357058c8a62ceab71db229 |
mit | ['generated_from_trainer'] | false | Training results | Epoch | Training Loss | Validation Loss | Accuracy | |:-------------:|:-----:|:---------------:|:--------:| | 1 | No log | 0.780298| 0.663366| | 2 | 0.827000 | 0.739960| 0.705446| | 3 | 0.597100 |0.775997 | 0.737624| | 4 | 0.413100 |0.860354 0. 767327 | | 5 | ... | a076b16d227c65f3b46cd5f583d33bed |
apache-2.0 | ['speech-to-text', 'hf-asr-leaderboard'] | false | xls-r-300m-danish-nst-cv9 This is a version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) finetuned for Danish ASR on the training set of the public NST dataset and the Danish part of Common Voice 9. The model is trained on 16kHz, so ensure that you use the same sample rate. ... | 2843ebbb3c418e9b0c10e49267eaaa67 |
apache-2.0 | ['speech-to-text', 'hf-asr-leaderboard'] | false | Performance The table below shows the WER rate of four different Danish ASR models on three publicly available datasets (lower is better). |Model | [Alvenir](https://huggingface.co/datasets/Alvenir/alvenir_asr_da_eval)| [NST](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-n... | 2df3c957dd91bc85826760b1d798ef30 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 700 - mixed_precisio... | 6ec487db50f951d8c60b7f128b85a2e6 |
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