license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['Instagram', 'NER', 'Named Entity Recognition', 'Food Entity Extraction', 'Social Media', 'Informal text', 'RoBERTa'] | false | Model description **InstaFoodRoBERTa-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** of Food entities on informal text (social media like). It has been trained to recognize a single entity: food (FOOD). Specifically, this model is a *roberta-base* model that was fine-tuned on a... | c5749c6a9b346f80452278b24150d8b4 |
mit | ['Instagram', 'NER', 'Named Entity Recognition', 'Food Entity Extraction', 'Social Media', 'Informal text', 'RoBERTa'] | false | How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Dizex/InstaFoodRoBERTa-NER") model = AutoModelForTokenClassification.from_pretrained(... | 0aef75a867e355461fe9b7f4daf8b8fa |
apache-2.0 | ['automatic-speech-recognition', 'id'] | false | exp_w2v2t_id_hubert_s784 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is... | 393c3bc40461f00be8b611c46f73559c |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-base-wikinewssum-all-languages This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2454 - Rouge1: 8.3826 - Rouge2: 3.5524 - Rougel: 6.8656 - Rougelsum: 7.8362 | 9b06f29b56b50317416d56fbc82967b9 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epo... | 842e54bc738cf35bd843cc1723d03ad2 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:| | No log | 1.0 | 3467 | 2.4034 | 8.0363 | 3.2484 | 6.5409 | 7.477 | | No log | 2.0 | 69... | c90ec71b9aea60796843d60d6d9aa486 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-imdb-target-imdb This model is a fine-tuned version of [muhtasham/small-mlm-imdb](https://huggingface.co/muhtasham/small-mlm-imdb) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3145 - Accuracy: 0.9174 - F1: 0.9569 | 6093078aade90a0fdc02ce73ee09aecd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.315 | 0.64 | 500 | 0.1711 | 0.9310 | 0.9642 | | 0.2248 | 1.28 | 1000 | 0.1385 | 0.9471 | 0.9728 | | 0.1824 |... | 8a17840d8151575891909a5f1916e3e7 |
other | ['stable-diffusion', 'text-to-image'] | false | ใๅฉ็จใฎ้ใฏไธ่จใฎใฉใคใปใณในๅ
ๅฎนใๅๅใซใ็ขบ่ชใใ ใใใ DeDeDePใฏๅ
ใซใใใขใใซใงใใ[DeDeDe](https://huggingface.co/nakayama/DeDeDe)ใจๆฏ่ผใใฆใใใใใฉใใชใขใซใชใขใใก่ชฟใฎ็ปๅใๅบๅใใใใใใใซ่ชฟๆดใใใStable Diffusionใขใใซใงใใ [DreamLike Diffusion 1.0](https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0)ใ[Trinart Characters v2 Derrida](https://huggingface.co/naclbit/trinart_... | c10522c1df9b7dcc41e367ad2722f18d |
other | ['stable-diffusion', 'text-to-image'] | false | ไพ <img src="https://huggingface.co/nakayama/DeDeDeP/resolve/main/img/image01.png" style="max-width:400px;" width="50%"/> ``` (((best quality, masterpiece))), detailed ((anime)) style of 1girl cowboy shot with detailed wavy pink hair pink and detailed yellow eye yellow in summer London river with picturesque, cinemati... | 6335f012a5526a6aa81822ac24cc17e8 |
other | ['stable-diffusion', 'text-to-image'] | false | ใฉใคใปใณในใซใคใใฆ ๅฝใขใใซใฏDreamlike Diffusion 1.0 / Dreamlike Photoreal 1.0ใฎๅฝฑ้ฟไธใซใใใใใไธ่จใขใใซใซใใใ**ไฟฎๆญฃใใใ**CreativeML OpenRAIL-M licenseใ้ฉ็จใใใพใใ ไปฅไธใฏDeepLใง็ฟป่จณใใใไฟฎๆญฃๅใฎๆฅๆฌ่ช่จณใจใชใใพใใใ่งฃ้ใซใใใฆๅชๅ
ใใใ่จ่ชใฏ่ฑ่ชใจใชใใพใใ - **ใใชใใๅๅ
ฅใๅฏไปใๅพใใใพใใฏๅพใไบๅฎใฎใฆใงใใตใคใ/ใขใใช/ใใฎไปใงใใใฎใขใใซใใใฎๆดพ็็ฉใใในใใใใไฝฟ็จใใใใใใใจใฏใงใใพใใใใใใใใใใใฎใชใใcontact@dreamlike.art ใพใงใกใผใซใใฆใใ ใใใ** - **... | 60f8d5e46157ae44073170002c81f0e4 |
cc-by-4.0 | [] | false | This model is a finetuned GPT-2 model on a small corpora of tweets about Paxlovid and Ivermectin. It is designed to be a "hello world" model to be used in conjunction with the "ModelExplorer" App that is part of the GitHub [KeywordExplorer](https://github.com/pgfeldman/KeywordExplorer) repository. The key feature of ... | 0c578ad90ccc8986a8df834963ef439f |
apache-2.0 | ['6th', 'generated_from_trainer'] | false | ff_analysis_3 This model is a fine-tuned version of [zdreiosis/ff_analysis_2](https://huggingface.co/zdreiosis/ff_analysis_2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0060 - F1: 1.0 - Roc Auc: 1.0 - Accuracy: 1.0 | ba037067821e4c39523cbdb8be5ceea3 |
apache-2.0 | ['6th', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | No log | 1.02 | 50 | 0.0138 | 1.0 | 1.0 | 1.0 | | No log | 2.04 | 100 | 0.0132 | 0.9966 ... | 0621d31cf8fb54739f9948106afbfcb4 |
apache-2.0 | ['multiberts', 'multiberts-seed_22'] | false | MultiBERTs - Seed 22 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 random seeds, which causes variatio... | 7bc40293867ccde1ae8b982fdfe0f198 |
apache-2.0 | ['multiberts', 'multiberts-seed_22'] | 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_22') model = TFBertModel.from_pretrained("google/multiberts-seed_... | 924db16cb4c4adfa7ca47f575d6958ff |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_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: 0.5404 - Accuracy: 0.4621 | f04f2e69ec76596a7d9c1f9f66f31f6c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2735 | 1.0 | 1136 | 0.5753 | 0.4657 | | 0.2182 | 2.0 | 2272 | 0.5404 | 0.4621 | | 0.2119 | 3.0 | 3408 | 0.5687 | 0.... | 85bf35528b7258fc8b7bfbfd211565fe |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum-epoch4 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 0.0 | e6d950665ef4df018988463a64e2bae8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.0 | 1.0 | 6377 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 ... | 4a5721022f6f30c596dbfbdd2c552a3e |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-patch16-224-in21k-finetuned-cassava This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.3742 - Accuracy: 0.8706 | 6bef1711f5e01b92cc15b151090a35ac |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5628 | 1.0 | 150 | 0.5357 | 0.8308 | | 0.4398 | 2.0 | 300 | 0.4311 | 0.8598 | | 0.4022 | 3.0 | 450 | 0.3958 | 0.... | 5ff7d5114033d6f943d1054496766a2c |
mit | [] | false | galaxy-explorer on Stable Diffusion This is the `<galaxy-explorer>` 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. Yo... | bc44ffcbe749b97006b95109320fab48 |
mit | [] | false | ํ์ต ํ๊ฒฝ ๋ฐ ํ์ดํผํ๋ผ๋ฏธํฐ - TPU V2-8 - Learning Rate: 3e-4, Batch Size: 512(=64 accum x 8 devices), Scheduler: Linear, WarmUp: 1000 step - Optimizer: AdamW(adam_beta1=0.9 adam_beta2=0.98, weight_decay=0.01) - bfloat16 - Training Steps: 43247 (3 epoch) - ํ์ต ํ ํฐ ์: 21.11B (43247 * 512 * 1024seq / 1024^3) - ํ์ต ๊ธฐ๊ฐ: 2023/1/30 ~ 2023/... | 5c48c339a478e3978eb21bb2909a5b6e |
mit | [] | false | ์ฌ์ฉ ์์ ```python from transformers import pipeline model_name = "heegyu/ajoublue-gpt2-medium" pipe = pipeline('text-generation', model=model_name) print(pipe("์๋
ํ์ธ์", repetition_penalty=1.2, do_sample=True, eos_token_id=1, early_stopping=True, max_new_tokens=128)) print(pipe("์ค๋ ์ ๋ถ ๋ฐํ์ ๋ฐ๋ฅด๋ฉด, ", repetition_penalty=1.2,... | b628ccec1e12c7e7d6ef0e249712a2ed |
apache-2.0 | ['generated_from_keras_callback'] | false | AdwayK/hugging_face_biobert_MLMAv2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0 - Validation Loss: 0.0839 - Epoch: 9 | 4b07c8c184fb3036faac3e92e8e74a7d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0 | 0.0571 | 0 | | 0.0 | 0.0601 | 1 | | 0.0 | 0.0598 | 2 | | 0.0 | 0.0652 | 3 | | 0.0 | 0.0718 | 4 | | 0.0 |... | b1c57f1fe985602bdf64165c78aed7ba |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-urdu-colab-cv8 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.4651 - Wer: 0.7 | 9bffc9a51398610e7cc2b4643e62187a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 20.3271 | 1.27 | 32 | 20.3487 | 1.0 | | 11.0206 | 2.55 | 64 | 7.7343 | 1.0 | | 5.8023 | 3.82 | 96 | 5.4188 | 1.0 | |... | f86f3ae220950d738ec04a3c709885c9 |
apache-2.0 | ['Generative QA', 'LFQA', 'ELI5', 'facebook/bart-large'] | false | Model description A Fusion in Decoder(FiD) model based on BART for the [KILT-ELI5](https://github.com/facebookresearch/KILT) task. The FiD model was in introduced in the paper 'Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering'. This model has been initialized with [facebook/bar... | 1a687929ecd31b5e3ac2931e44767b61 |
apache-2.0 | ['Generative QA', 'LFQA', 'ELI5', 'facebook/bart-large'] | false | Intended uses & limitations You can use this raw model for the generative question answering task. Biases associated with the pre-existing language model that we used, facebook/bart-large, may be present in our fine-tuned model. | 58160616f72d4f284cb37bd8aaffe945 |
apache-2.0 | ['Generative QA', 'LFQA', 'ELI5', 'facebook/bart-large'] | false | BibTeX entry and citation info ```bibtex @inproceedings{Izacard2021LeveragingPR, title={Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering}, author={Gautier Izacard and Edouard Grave}, booktitle={EACL}, year={2021} } ``` ```bibtex @inproceedings{petroni-etal-2021-kilt, ... | 272c59854db904eec8aedf15368e727c |
apache-2.0 | ['automatic-speech-recognition', 'id'] | false | exp_w2v2t_id_unispeech-sat_s287 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your spee... | 097957331094bebd85d70ebe74b845d9 |
apache-2.0 | ['translation'] | false | nor-nor * source group: Norwegian * target group: Norwegian * OPUS readme: [nor-nor](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-nor/README.md) * model: transformer-align * source language(s): nno nob * target language(s): nno nob * model: transformer-align * pre-processing: normaliz... | f9c7cb87f29249c91771d55de6d2de30 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: nor-nor - source_languages: nor - target_languages: nor - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-nor/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['no'] - src_constituents: {'nob', 'nno'} - tgt_cons... | c93eb3127a91111ffddb170fee9f2785 |
apache-2.0 | ['SetFitbench', 'generated_from_trainer'] | false | setfit_bench_bert-base-uncased_finetuned_for_seqclassif This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2666 | 538c949a9dcee183de31ba3df0a1268b |
mit | ['text-generation'] | false | Tokenizer We first trained a tokenizer on OSCAR's `unshuffled_original_fr` French data subset by following the training of GPT2 tokenizer (same vocab size of 50,257). Here's the [Python file](https://github.com/bigscience-workshop/multilingual-modeling/blob/gpt2-fr/experiments/exp-001/train_tokenizer_gpt2.py) for the ... | fa60efa2799c5823918763068ed4242a |
mit | ['text-generation'] | false | Model We finetuned the `wte` and `wpe` layers of GPT-2 (while freezing the parameters of all other layers) on OSCAR's `unshuffled_original_fr` French data subset. We used [Huggingface's code](https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_clm.py) for fine-tuning the caus... | 9437f81f72a2170a39acd5eff22b2418 |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model: ```python >>> from transformers import AutoFeatureExtractor, RegNetForImageClassification >>> import torch >>> from datasets import load_dataset >>> dataset = load_dataset("huggingface/cats-image") >>> image = dataset["test"]["image"][0] >>> feature_extractor = AutoFeature... | ab6287262f21801e451827e3a0e0300e |
apache-2.0 | ['bart', 'seq2seq', 'summarization'] | false | Example 1 ```python from transformers import pipeline summarizer = pipeline("summarization", model="knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM") text = '''The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, me... | 6cc58eb929664646eeb32e195d3d2b3a |
apache-2.0 | ['bart', 'seq2seq', 'summarization'] | false | Example 2 ```python from transformers import pipeline summarizer = pipeline("summarization", model="knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM") text = '''Bangalore is the capital and the largest city of the Indian state of Karnataka. It has a population of more than 8 million and a metropolitan popul... | 5386c3289e39a189589c4576e4baa101 |
apache-2.0 | ['bart', 'seq2seq', 'summarization'] | false | Example 3 ```python from transformers import pipeline summarizer = pipeline("summarization", model="knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM") text = '''Hi, I'm David and I'm supposed to be an industrial designer. Um, I just got the project announcement about what the project is. Designing a remote ... | 96f7f02a8d703d61eaa94c6b822e6f20 |
apache-2.0 | ['bart', 'seq2seq', 'summarization'] | false | Example 4 ```python from transformers import pipeline summarizer = pipeline("summarization", model="knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM") text = ''' Das : Hi and welcome to the a16z podcast. Iโm Das, and in this episode, I talk SaaS go-to-market with David Ulevitch and our newest enterprise gen... | dd19ce41c17237e59c7f793a2691bf19 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Swedish This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3442 - Wer: 19.9430 Check [here](https://drive.google.com/file/d/10Nd0rMnLM5yEpMhI26sHVp... | d39f3adf5a243add57b7da0f82cb66e3 |
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: 16 - 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_precision_trainin... | 5982b35e393d573e3981d39b7e95be0e |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT', 'RobBERTje'] | false | <p align="center"> <img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%"> </p> | fc86232a3be004e080ae9096f79f3087 |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT', 'RobBERTje'] | false | About RobBERTje RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robbert). There are multiple models with different sizes and different training settings, which you can choose for your use-case. We are also continuously working on releasing better-performing models, so watch ... | 67b39c9a3622c8cfe5e244f4fdf9c62c |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT', 'RobBERTje'] | false | News - **February 21, 2022**: Our paper about RobBERTje has been published in [volume 11 of CLIN journal](https://www.clinjournal.org/clinj/article/view/131)! - **July 2, 2021**: Publicly released 4 RobBERTje models. - **May 12, 2021**: RobBERTje was accepted at [CLIN31](https://www.clin31.ugent.be) for an oral presen... | dc858319204515c74d22112c648f07be |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT', 'RobBERTje'] | false | The models | Model | Description | Parameters | Training size | Huggingface id | |--------------|-------------|------------------|-------------------|------------------------------------------------------------------------------------| | Non-sh... | 71880f243c018d44329da35f5506fde3 |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT', 'RobBERTje'] | false | Intrinsic results We calculated the _pseudo perplexity_ (PPPL) from [cite](), which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution. | Model | PPPL | |-------------------|-----------| | RobBERT (teacher) | 7.76 ... | 4d39ad0292ff7c20f6afe35cee30da2f |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT', 'RobBERTje'] | false | Extrinsic results We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a [Dutch NLI task named SICK-NL](https://arxiv.org/abs/2101.05716) was also released and we evaluated our models with it as well. | Model | DBRD | DIE-DAT | NER ... | c386c3402b6caa9c3b5c29b37fcb922a |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | 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 - MN dataset. It achieves the following results on the evaluation set: - Loss: 0.5502 - Wer: 0.4042 | 09e4d76ad951fb8374d9d8ea345e191b |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training and evaluation data Evaluation is conducted in Notebook, you can see within the repo "notebook_evaluation_wav2vec2_mn.ipynb" Test WER without LM wer = 58.2171 % cer = 16.0670 % Test WER using wer = 31.3919 % cer = 10.2565 % How to use eval.py ``` huggingface-cli login | 4b02fbd4ade64a18eac77ae66b84ffdc |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 8 - 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_sch... | 75c35325c717e4a7a1c79a4e59c7a528 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 6.35 | 400 | 0.9380 | 0.7902 | | 3.2674 | 12.7 | 800 | 0.5794 | 0.5309 | | 0.7531 | 19.05 | 1200 | 0.5749 | 0.4815 | |... | 50c271a96d32efc82ecf956de9ae49f6 |
apache-2.0 | ['text-classfication', 'int8', 'onnx'] | false | Model Details **Model Description:** This model is a [DistilBERT](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) fine-tuned on SST-2 dynamically quantized with [optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [huggingface/optimum-intel](https://github.com/hugg... | 80c609c5cdfa33d1bb8fd996166872e1 |
apache-2.0 | ['text-classfication', 'int8', 'onnx'] | false | PyTorch To load the quantized model, you can do as follows: ```python from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification model = IncQuantizedModelForSequenceClassification.from_pretrained("Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic") ``` | a75a1dbf79ad269bb5f68b2d92a7a550 |
apache-2.0 | ['text-classfication', 'int8', 'onnx'] | false | ONNX This is an INT8 ONNX model quantized with [Intelยฎ Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [DistilBERT](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english). | d611226f058813984a5706d814a597b8 |
apache-2.0 | ['text-classfication', 'int8', 'onnx'] | false | Load ONNX model: ```python from optimum.onnxruntime import ORTModelForSequenceClassification model = ORTModelForSequenceClassification.from_pretrained('Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic') ``` | 75aa997c3e90d0fcb8ab9164722c5e02 |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-zh-de-tuned-Tatoeba-small This model is a fine-tuned version of [Helsinki-NLP/opus-mt-zh-de](https://huggingface.co/Helsinki-NLP/opus-mt-zh-de) on a refined dataset of Tatoeba German - Chinese corpus https://github.com/Helsinki-NLP/Tatoeba-Challenge/blob/master/data/README.md. It achieves the following result... | 0536e14276395e1292ccdd81d9b0dae0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:| | 2.7229 | 0.24 | 16000 | 2.5605 | 14.1956 | 16.2206 | | 2.5988 | 0.49 | 32000 | 2.4447 | 14.8619 | 16.2726 | | 2.515 ... | 85b811f98758f2c962337a469a57a262 |
apache-2.0 | ['generated_from_trainer'] | false | mi-modelo-bacan-test This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3318 - Accuracy: 0.8767 - F1: 0.8825 | bf685bb741c476b5c32635178baa8984 |
apache-2.0 | ['generated_from_keras_callback'] | false | nst-sat/bert-base-uncased-finetuned This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.7477 - Epoch: 0 | f93c8a6a9f256bb2e1819780248697e2 |
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... | 91ef0de17915047a1bbb892c406c3071 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 495 | 1.6826 | 27.5191 | 15.0672 | 23.3065 | 24.7163 | 20... | a9a99b4b6ea2c64e7cc6473a4438b458 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Ko(FLUERS) - by p4b This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the FLUERS Korean dataset. It achieves the following results on the evaluation set: - Loss: 0.4512 - Wer: 148.1005 | e80f008ad1efdd0d35f2bcab43b09c88 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 96 - eval_batch_size: 64 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - train... | 83cabbad57ee3a8a1891344a68e5ff34 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6003 | 32.0 | 800 | 0.5913 | 167.2749 | | 0.459 | 64.0 | 1600 | 0.4978 | 170.9841 | | 0.4035 | 96.0 | 2400 | 0.4653 | 16... | 3d990441fc10c0ddb0d036fb6cb4e5ae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 44 | 3.0933 | | No log | 2.0 | 88 | 2.7392 | | No log | 3.0 | 132 | 2.6449 | | cafaa1ad8c2e135e9a1ff615b6682802 |
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: 0.2183 - Accuracy: 0.9245 - F1: 0.9246 | cfc22123ac48d8234a19c2ef33b2b898 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8174 | 1.0 | 250 | 0.3166 | 0.905 | 0.9023 | | 0.2534 | 2.0 | 500 | 0.2183 | 0.9245 | 0.9246 | | 5bb770a68bf490b13c3677a611fb7e2f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4642 | b0f3acef08c242f6c2786d0e53dab206 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6835 | 1.0 | 157 | 2.5426 | | 2.5874 | 2.0 | 314 | 2.4668 | | 2.5288 | 3.0 | 471 | 2.4689 | | e57f0c0a6e0c7884765044c610878f4f |
apache-2.0 | ['generated_from_trainer'] | false | distilbart-cnn-12-3-finetuned-pubmed This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-3](https://huggingface.co/sshleifer/distilbart-cnn-12-3) on the pub_med_summarization_dataset dataset. It achieves the following results on the evaluation set: - Loss: 2.1743 - Rouge1: 40.5642 - Rouge2: 16.9812 - R... | 67dc6df99b182b37de11412c322fa118 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 2.469 | 1.0 | 4000 | 2.2956 | 38.3713 | 15.2594 | 23.6734 | 34.1634 ... | 6e9cce737e6e4a82f37dddc9fd3695ae |
mit | ['generated_from_trainer'] | false | ar_xlmr-large This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3861 | 954ba7455ec6d8828589aeb596a4e5c9 |
mit | ['summarization'] | false | bart-base-cnn-xsum-swe This model is a fine-tuned version of [Gabriel/bart-base-cnn-swe](https://huggingface.co/Gabriel/bart-base-cnn-swe) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1027 - Rouge1: 30.9467 - Rouge2: 12.2589 - Rougel: 25.4487 - Rougelsum: 25.4792 - Gen Len: ... | a7ffc1548262529bb926932c0610ae9b |
mit | ['summarization'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | e56fca1233c2da11b34a42b27116ac86 |
mit | ['summarization'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.3076 | 1.0 | 6375 | 2.1986 | 29.7041 | 10.9883 | 24.2149 | 24.2406 |... | bec77c998c7e29846fb450357b21b305 |
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: 0.6787 - Accuracy: 0.86 - F1: 0.8636 | 893e286e4c775379add7fd37d67891fc |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-zh_TW This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 1.0047 - Bleu: 39.0863 | d40f9fd22ee0776273ea370ae2adf027 |
apache-2.0 | ['translation'] | false | opus-mt-da-fi * source languages: da * target languages: fi * OPUS readme: [da-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/da-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | ac44b30b049508a296040e1589b09a3d |
apache-2.0 | ['generated_from_trainer'] | false | run1 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-es](https://huggingface.co/Helsinki-NLP/opus-mt-es-es) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 3.1740 - Bleu: 8.4217 - Gen Len: 15.9457 | b16eb88dd15a023133da0dd84cd7b8ef |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP | 18a0d3ee3612978e46e3fc4c6d32946c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 250 | 4.2342 | 0.8889 | 83.4022 | | 4.6818 | 2.0 | 500 | 3.7009 | 4.1671 | 35.587 | | 4.6818 | 3.0... | d2540b2fcf7f6c5d172516d2d7bbc960 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | <p align="center"><img src="https://huggingface.co/0RisingStar0/HighRiseMixV1/resolve/main/00401-2269441947-(masterpiece%2C%20excellent%20quality%2C%20high%20quality%2C%20highres%20_%201.5)%2C%20(1girl%2C%20solo)%2C%20solo%20focus%2C%20sky%2C%20city%2C%20skyscrapers%2C%20pavement%2C%20tree.png"> <img src="https://hugg... | e94bb6c4eb3415cd8e2b06afcafb2496 |
apache-2.0 | ['translation'] | false | opus-mt-en-lun * source languages: en * target languages: lun * OPUS readme: [en-lun](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-lun/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | 259eb049c616bbec98b2c943be071f7e |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | distilroberta-base-mrpc-glue-oscar-salas2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset. It achieves the following results on the evaluation set: - Loss: 1.5094 | 554545f579219bb391a8471121dfee11 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'es', 'robust-speech-event', 'hf-asr-leaderboard'] | false | wav2vec2-cls-r-300m-es 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 - ES dataset. It achieves the following results on the evaluation set: - Loss: 0.5160 - Wer: 0.4016 | 794718975fc386f6cca7f5bb57180bc4 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'es', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8.0 - mixed_precision_training: Native AMP | e7b3b375c59f14137443b256b3810cc2 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'es', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.1277 | 1.14 | 500 | 2.0259 | 0.9999 | | 1.4111 | 2.28 | 1000 | 1.1251 | 0.8894 | | 0.8461 | 3.42 | 1500 | 0.8205 | 0.7244 | |... | 45600fb88954c79f7771d585b234b59f |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'es', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test` ```bash python eval.py --model_id samitizerxu/wav2vec2-xls-r-300m-es --dataset mozilla-foundation/common_voice_7_0 --config es --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python ev... | 85b7ad23be83fd5f36f93616e5777b2c |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1713 - F1: 0.8544 | 2535f1291c32bd23807e7f4713dc68cf |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3076 | 1.0 | 835 | 0.2008 | 0.7923 | | 0.1565 | 2.0 | 1670 | 0.1809 | 0.8437 | | 0.1027 | 3.0 | 2505 | 0.1713 | 0.8544 | ... | 0dd0eda343ecd9356c22af92f7a6335e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53-english This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3950 - Wer: 0.3496 | 09469c545e53668aafdf1d5191fa4e68 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4499 | 1.0 | 500 | 1.4091 | 0.9755 | | 0.7934 | 2.01 | 1000 | 0.5071 | 0.5282 | | 0.43 | 3.01 | 1500 | 0.4686 | 0.4661 | |... | 3424c8d6ffeb81cfbcf467938c6f4de3 |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_hubert_s995 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is... | 2781056062d4bfbf41fa787e62053ef0 |
apache-2.0 | ['generated_from_trainer'] | false | mbart_jokes This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0282 | c88c243aac7dfed4f3c2c2f2b7650785 |
apache-2.0 | ['vision', 'image-classification'] | false | Swin Transformer v2 (small-sized model) Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/micro... | d0431dbdd6c30b97372fa9765023f869 |
apache-2.0 | ['vision', 'image-classification'] | false | How to use 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 AutoImageProcessor, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" i... | 07b01a04a5917a30b05bb0acf13bde8d |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.