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 | resnet-18-feature-extraction This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1485 - Accuracy: 0.95 - Precision: 0.9653 - Recall: 0.9789 - F1: 0.9720 - Roc Auc: 0.8505 ... | 53781a1b1c2e20a528c7c0aae58866ee |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 6dcf0b64b109b7327170df717a37973b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| | No log | 0.8 | 2 | 0.6232 | 0.75 | 0.9636 | 0.7465 | 0.8413 | 0.762... | e2ea6a9170e04d8aac89d4a93e48d579 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_transport-roberta-large-v1-5-3 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with con... | 89e3d5b7aaca5528bfd5ea79d9ee74ab |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-en-es Neural machine translation model for translating from English (en) to Spanish (es). 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... | ed85026471391faa280ac02f02425a93 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-13 * source language(s): eng * target language(s): spa * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](htt... | e3fd1bc4a0dd3da1c88ca1d25486733a |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "A wasp stung him and he had an allergic reaction.", "I love nature." ] model_name = "pytorch-models/opus-mt-tc-big-en-es" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel... | 07ff2f24813f1228d860a3101f35121f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Me encanta la naturaleza. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-es") print(pipe("A wasp stung him and he had an allergic reaction.")) | 31c71612817fa9e3e1b16843b14d01c2 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-... | e20a7123b92dbf0c7c02da0adfc0a5fd |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | eng-spa | tatoeba-test-v2021-08-07 | 0.73863 | 57.2 | 16583 | 134710 | | eng-spa | flores101-devtest | 0.56440 | 28.5 | 1012 | 29199 | | eng-spa | newssyscomb2009 | 0.58415 | 31.5 | 502 | 12503 | | eng-spa | news-test2008 | 0.56707 | 30.1 | 2051 | 52586 ... | a3fe0d610e1a03e2531995816142d2cd |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Russian-GSD | Feature | Description | | --- | --- | | **Name** | `ru_udv25_russiangsd_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit... | 56c2eaf6bb05b45b0c55e71b7d7b7603 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (3014 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `!`, `& | 742998dde8e51880858b383935526602 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | 39;`, `'`, `(`, `)`, `,`, `-`, `--`, `.`, `.,`, `/`, `:`, `AFX`, `APOSTROPHE`, `AWP`, `CC`, `CD`, `DT`, `FW`, `IN`, `JJ`, `JJH`, `JJL`, `JJR`, `JJRL`, `JJS`, `NEG`, `NFP`, `NN`, `NNP`, `ORD`, `PRED`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `UH`, `VB`, `VBC`, `VBG`, `VBNH`, `VBNL`, `WDT`, `WP`, `WRB`, `X`, ```` ... | c53781c413a47b5c0a8a2e59cd66e60a |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.49 | | `TOKEN_P` | 99.48 | | `TOKEN_R` | 99.50 | | `TOKEN_ACC` | 99.94 | | `SENTS_F` | 96.05 | | `SENTS_P` | 95.56 | | `SENTS_R` | 96.55 | | `TAG_ACC` | 96.91 | | `POS_ACC` | 98.25 | | `MORPH_ACC` | 94.72 | | `DEP_UAS` | 92.10 | | `DEP_LAS` | 88.72 | | `LEMMA_A... | 69c43bcb183511c5148ede7f4d4f9b2e |
cc-by-4.0 | [] | false | MahaNER-BERT MahaNER-BERT is a MahaBERT(l3cube-pune/marathi-bert) model fine-tuned on L3Cube-MahaNER - a Marathi named entity recognition dataset. [dataset link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/... | a11d2efb430bc8c277de5679fa2c1744 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2723 - F1: 0.8340 | 2bb3d30d429bb99e69370f7d79802601 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5909 | 1.0 | 191 | 0.3404 | 0.7891 | | 0.2594 | 2.0 | 382 | 0.2919 | 0.8152 | | 0.1752 | 3.0 | 573 | 0.2723 | 0.8340 | ... | a2415f2cff8abca8702cc4dbff22e607 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_vp-sv_s44 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your... | 884e427f00be5ac0cbc1c06a86797b37 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-mrpc-custom-tokenizer-target-glue-qnli This model is a fine-tuned version of [muhtasham/small-mlm-glue-mrpc-custom-tokenizer](https://huggingface.co/muhtasham/small-mlm-glue-mrpc-custom-tokenizer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4493 - Accuracy: 0... | 92d887298662f6553ede63e60651e7c7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5672 | 0.15 | 500 | 0.4950 | 0.7650 | | 0.532 | 0.31 | 1000 | 0.4894 | 0.7710 | | 0.5191 | 0.46 | 1500 | 0.5007 | 0.... | 4994e67dd09954a5b6cc8579b24d5d05 |
mit | ['grammar-correction'] | false | T5-Efficient-TINY for grammar correction This is a [T5-Efficient-TINY](https://huggingface.co/google/t5-efficient-tiny) model that was trained on a subset of [C4_200M](https://ai.googleblog.com/2021/08/the-c4200m-synthetic-dataset-for.html) dataset to solve the grammar correction task in English. To bring additional ... | 69c2116b9c6dc4aef6bf417701bd87f6 |
mit | [] | false | black-waifu on Stable Diffusion This is the `<black-waifu>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can al... | 558e3b81997913c6323fe9afb76109d9 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-finetuned-amazon-en-es 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: 3.0285 - Rouge1: 16.9728 - Rouge2: 8.2969 - Rougel: 16.8366 - Rougelsum: 16.851 - Gen Len: 10.1597 | c1b8c889107310692b1ca32b0982eee3 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 7.1016 | 1.0 | 1209 | 3.3069 | 13.9858 | 5.8437 | 13.6053 | 13.5125 | 8.378... | 8d8733e74f0ebb82119c3d790509c663 |
apache-2.0 | ['dutch', 'english', 't5', 't5x', 'ul2', 'seq2seq'] | false | ul2-base-dutch-english for Dutch and English Pretrained T5 model on Dutch and English using a UL2 (Mixture-of-Denoisers) objective. The T5 model was introduced in [this paper](https://arxiv.org/abs/1910.10683) and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer). The... | 46228bc682e997ca4d172c39ca22bc85 |
apache-2.0 | ['dutch', 'english', 't5', 't5x', 'ul2', 'seq2seq'] | false | Model description T5 is an encoder-decoder model and treats all NLP problems in a text-to-text format. `ul2-base-dutch-english` T5 is a transformers model pretrained on a very large corpus of Dutch and English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labell... | db36b5fa4a52a4602e01d2291bbeb426 |
apache-2.0 | ['dutch', 'english', 't5', 't5x', 'ul2', 'seq2seq'] | false | How to use Here is how to use this model in PyTorch: ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("yhavinga/ul2-base-dutch-english", use_fast=False) model = T5ForConditionalGeneration.from_pretrained("yhavinga/ul2-base-dutch-english") ``` and in... | 56c03edf426d4c0276ca25241c988fb4 |
apache-2.0 | ['dutch', 'english', 't5', 't5x', 'ul2', 'seq2seq'] | false | Training data The `ul2-base-dutch-english` T5 model was pre-trained simultaneously on a combination of several datasets, including the `full_en_nl` config of the "mc4_nl_cleaned" dataset, which is a cleaned version of Common Crawl's web crawl corpus, Dutch books, the Dutch subset of Wikipedia (2022-03-20), the Englis... | d06419aecb3986f316490679da8f0504 |
apache-2.0 | ['dutch', 'english', 't5', 't5x', 'ul2', 'seq2seq'] | false | Preprocessing The ul2-base-dutch-english T5 model uses a SentencePiece unigram tokenizer with a vocabulary of 32,000 tokens. The tokenizer includes the special tokens `<pad>`, `</s>`, `<unk>`, known from the original T5 paper, `[NLU]`, `[NLG]` and `[S2S]` for the MoD pre-training, and `<n>` for newline. During pre-t... | 6fe6625b552c8e09e658593116b1d9ff |
apache-2.0 | [] | false | Model Description This model is russian version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased). The code for the transforming process can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/spellchecker/ml_ranging/models/distilbert_base_russ... | 173b4080b4a6c765e50e8a530d4dd349 |
apache-2.0 | [] | false | How to Get Started With the Model You can use the model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> pipeline = pipeline('fill-mask', model='DmitryPogrebnoy/distilbert-base-russian-cased') >>> pipeline("Π― [MASK] Π½Π° Π·Π°Π²ΠΎΠ΄Π΅.") [{'score': 0.1149893701076507... | fcd94c03395e6303823029ef81590cab |
apache-2.0 | [] | false | ΡΠΎΡΠ»Π°', 'sequence': 'Π―ΡΠΎΡΠ»Π° Π½Π° Π·Π°Π²ΠΎΠ΄Π΅.'}, {'score': 0.03575785085558891, 'token': 4059, 'token_str': 'Π½Π°Ρ
ΠΎΠ΄ΠΈΠ»ΡΡ', 'sequence': 'Π― Π½Π°Ρ
ΠΎΠ΄ΠΈΠ»ΡΡ Π½Π° Π·Π°Π²ΠΎΠ΄Π΅.'}, {'score': 0.02496381290256977, 'token': 5075, 'token_str': 'ΡΠ°Π±ΠΎΡΠ°Π΅Ρ', 'sequence': 'Π― ΡΠ°Π±ΠΎΡΠ°Π΅Ρ Π½Π° Π·Π°Π²ΠΎΠ΄Π΅.'}, {'score': 0.020675526931881905, 'token'... | ba00a6502493d2d59e36835eca1b3ee7 |
apache-2.0 | [] | false | Π΄ΡΠΎ', 'sequence': 'Π―Π΄ΡΠΎ Π½Π° Π·Π°Π²ΠΎΠ΄Π΅.'}] ``` Or you can load the model and tokenizer and do what you need to do: ```python >>> from transformers import AutoTokenizer, AutoModelForMaskedLM >>> tokenizer = AutoTokenizer.from_pretrained("DmitryPogrebnoy/distilbert-base-russian-cased") >>> model = AutoModelForMaskedLM.fro... | 0a04c4b85122a195607da9e022dfd483 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_vp-100k_age_teens-2_sixties-8_s304 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using th... | 6399899309e84473719c1c9482a39292 |
apache-2.0 | ['generated_from_trainer'] | false | hate_trained_31415 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.8507 - F1: 0.7719 | 2d702c873db6ca3b76b2622c6d882ccc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4817 | 1.0 | 563 | 0.4975 | 0.7678 | | 0.3311 | 2.0 | 1126 | 0.4965 | 0.7773 | | 0.2303 | 3.0 | 1689 | 0.7102 | 0.7613 | |... | 88633712e0b32479654ac5a1010ea8ef |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Punjabi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Punjabi using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. | da65dd139ad375365e1c7c1eac286e57 |
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", "pa-IN", split="test") processor = Wav2Vec2Processor.from_pre... | 809b07cda5715807e0742ebf3db74de4 |
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): \\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \\\\tbatch["speech"] = resampler(speech_array).squeeze().numpy() \\\\treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["s... | 1cece7f7ce00f70f8d0ed25a66d9ea15 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: replace language with your {language}, *e.g.* French ```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", "pa-IN", split="test") | 76a26734c36383f67071c1a676b48e10 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-punjabi") model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-p... | c93eac0c3a128d5a72b590aa58f87b03 |
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): \\\\\\\\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() \\\\\\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \\\\\\\\tbatch["speech"] = resampler(speech_array).squeeze().numpy() \\\\\\\\tret... | 2e1c11e5e10474a9e3205426a7706dcf |
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): \\\\\\\\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) \\\\\\\\twith torch.no_grad(): \\\\\\\\t\\\\\\\\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits ... | f7fbf46b5dad08db5e5cb1265054123b |
apache-2.0 | ['pytorch', 'text-generation', 'causal-lm', 'rwkv'] | false | Model Description RWKV-4 3B is a L32-D2560 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details. Use https://github.com/BlinkDL/ChatRWKV to run it. New checkpoint: RWKV-4-Pile-3B-20221110-ctx4096.pth : Fine-tuned to ctx_len = 4096 * LAMBADA ppl 5.25, acc 63.96% * PIQA acc 74... | 094d3cf91aedebccde12dfe954c7ffbc |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_1600k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 3, Step 1600k 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... | 88a12f129afdc72fd2ce986ee8bb39f7 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_1600k'] | 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_3-step_1600k') model = TFBertModel.from_pretrained("google/multib... | a2c943fb2b203a71cfaa7cc8a334e457 |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_vp-it_s738 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 45b9c4d9c1d46143e3a2525df0061234 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-misogyny 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: 0.7913 - Accuracy: 0.8925 - F1: 0.8280 - Precision: 0.8240 - Recall: 0.8320 - Mae: 0.1075 | 745b1bb17cba293134f1ef8e8e2a69e7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:| | 0.328 | 1.0 | 828 | 0.3477 | 0.8732 | 0.7831 | 0.8366 | 0.7359 | 0.1268 ... | 95f2e112a94f03eaef13643e10abded2 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `kan-bayashi/csj_asr_train_asr_transformer_raw_char_sp_valid.acc.ave` β»οΈ Imported from https://zenodo.org/record/4037458/ This model was trained by kan-bayashi using csj/asr1 recipe in [espnet](https://github.com/espnet/espnet/). | d97721381b0ac0d8c40b041e07510f26 |
mit | ['gec'] | false | Usage Install the necessary dependencies: ```bash pip3 install ctranslate2 pyonmttok ``` Simple tokenization & translation using Python: ```python import ctranslate2 import pyonmttok from huggingface_hub import snapshot_download model_dir = snapshot_download(repo_id="jordimas/gec-opennmt-english", revision="mai... | b1e025e9a25117e5aed857a16bef471a |
mit | ['gec'] | false | Model The model has been training using the [clang8](https://github.com/google-research-datasets/clang8) corpus for English language. Details: * Model: TransformerBase * Tokenizer: SentencePiece * BLEU = 85.50 | 79b1607696a04398ae61cb25782bd45b |
mit | ['gec'] | false | Papers Relevant papers: * [Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task](https://aclanthology.org/N18-1055.pdf) * [A Simple Recipe for Multilingual Grammatical Error Correction](https://arxiv.org/pdf/2106.03830.pdf) | a49d97a24d6cd16a7e9cb75affb04027 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_age_teens-0_sixties-10_s888 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure t... | 3733126fcb90eea38f8d2161900e97e4 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Kurmanji (kmr) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](h... | 4eef2397fe69c9e6de09bba3d223b520 |
apache-2.0 | ['generated_from_trainer'] | false | legal-roberta-base-filtered-cuad This model is a fine-tuned version of [saibo/legal-roberta-base](https://huggingface.co/saibo/legal-roberta-base) on the cuad dataset. It achieves the following results on the evaluation set: - Loss: 0.0428 | d0d9cfb8e08c1c2495ea507a1ee7ccc9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 22 - eval_batch_size: 22 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | fe5016abd9096eadc3d9b22066c6d675 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.0556 | 1.0 | 12279 | 0.0517 | | 0.0406 | 2.0 | 24558 | 0.0425 | | 0.0332 | 3.0 | 36837 | 0.0428 | | 42108ee09a7c0eff2f970c7c0b47ee52 |
apache-2.0 | ['translation'] | false | opus-mt-es-da * source languages: es * target languages: da * OPUS readme: [es-da](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-da/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 361ce890a274099c5e775c5e4f696a8c |
mit | ['generated_from_trainer'] | false | xlm-all-final This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tydiqa dataset. It achieves the following results on the evaluation set: - Loss: 0.6038 | b933b62f61a0c5424d48753ad7e85fdd |
mit | ['generated_from_trainer'] | false | roberta-base_mnli_uf_ner_1024_train_v0 This model is a fine-tuned version of [mariolinml/roberta-base_fullMnli_10_24_v0](https://huggingface.co/mariolinml/roberta-base_fullMnli_10_24_v0) on the None dataset. | 2cd8076f15b706c8434379fe6341a64f |
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: 0.2222 - Accuracy: 0.9255 - F1: 0.9257 | 12c594341ee9d288c4fb4440b382f31e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7962 | 1.0 | 250 | 0.3167 | 0.903 | 0.8984 | | 0.2475 | 2.0 | 500 | 0.2222 | 0.9255 | 0.9257 | | 47e86a704b0373d1f342b5c2973038ca |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2569 - F1: 0.8254 | c315950da44e9adb7eb208e0ac81ba07 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 105 | 0.3244 | 0.7521 | | No log | 2.0 | 210 | 0.2719 | 0.8104 | | No log | 3.0 | 315 | 0.2569 | 0.8254 | ... | ed59828aa4ebd92231e0d8aa0d3659b3 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-banking-11-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7470 - Accuracy: 0.0756 | e8ebce53665a2f2c06d8fcdc190cc542 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0573 - Precision: 0.9343 - Recall: 0.9495 - F1: 0.9418 - Accuracy: 0.9868 | 4a9f39d3e99ba62a4b44d0392ef75b3a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0854 | 1.0 | 1756 | 0.0639 | 0.9148 | 0.9329 | 0.9238 | 0.9822 | | 0.0403 | 2.0 |... | 48273d0c34c6c15f0f36cca6a7bcde67 |
apache-2.0 | ['translation'] | false | opus-mt-en-sw * source languages: en * target languages: sw * OPUS readme: [en-sw](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-sw/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 583a20d5ee29b2f2be130925dd35d594 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sst2_int8_xml This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4463 - Accuracy: 0.9037 | cedd28662fd13300c28920be3f86de82 |
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: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | 14779b122a7cd4cc27787ded0c56579f |
apache-2.0 | ['korean'] | false | KoELECTRA (Base Discriminator) Pretrained ELECTRA Language Model for Korean (`koelectra-base-discriminator`) For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md). | e7bd3812edb0c1f168b7ceac28d85ce3 |
apache-2.0 | ['korean'] | false | Load model and tokenizer ```python >>> from transformers import ElectraModel, ElectraTokenizer >>> model = ElectraModel.from_pretrained("monologg/koelectra-base-discriminator") >>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-discriminator") ``` | e3e2d305effaf0f00cf62ef1a06a4d3f |
apache-2.0 | ['korean'] | false | Tokenizer example ```python >>> from transformers import ElectraTokenizer >>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-discriminator") >>> tokenizer.tokenize("[CLS] νκ΅μ΄ ELECTRAλ₯Ό 곡μ ν©λλ€. [SEP]") ['[CLS]', 'νκ΅μ΄', 'E', ' | b8ba82c252e250ca7539296d4ebb007e |
apache-2.0 | ['korean'] | false | Example using ElectraForPreTraining ```python import torch from transformers import ElectraForPreTraining, ElectraTokenizer discriminator = ElectraForPreTraining.from_pretrained("monologg/koelectra-base-discriminator") tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-discriminator") sentence = ... | 20abb0cfaa7b55a31ccf66e26a1977ef |
mit | [] | false | pen-ink-portraits-BenNorthen on Stable Diffusion This is the `<ink-portrait-by-BenNorthern>` 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_infe... | 80cf13e2cda8c663fbe28251fd37efed |
apache-2.0 | ['automatic-speech-recognition', 'collectivat/tv3_parla', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'projecte-aina/parlament_parla', 'robust-speech-event'] | false | wav2vec2-xls-r-1b-ca-lm 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 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/dataset... | a203af1efff7ae2393fa8a5523c95725 |
apache-2.0 | ['automatic-speech-recognition', 'collectivat/tv3_parla', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'projecte-aina/parlament_parla', 'robust-speech-event'] | false | Training results Check the Tensorboard tab to check the training profile and evaluation results along training. The model was evaluated on the test splits for each of the datasets used during training. | 69c1ed64a775e179d593a950bce09577 |
apache-2.0 | ['automatic-speech-recognition', 'collectivat/tv3_parla', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'projecte-aina/parlament_parla', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | f46f8d13330db34f834ce4564d409d66 |
apache-2.0 | ['text-classification', 'generic'] | false | Hugging Face Transformers with Scikit-learn Classifiers π€©π This repository contains a small proof-of-concept pipeline that leverages longformer embeddings with scikit-learn Logistic Regression that does sentiment analysis. The training leverages the language module of [whatlies](https://github.com/koaning/whatlies... | 07a387512d7d5220fc58551e547f9c30 |
apache-2.0 | ['text-classification', 'generic'] | false | Classification Report π Below is the classification report ππ» ``` precision recall f1-score support 0 0.85 0.89 0.87 522 1 0.89 0.85 0.87 550 accuracy 0.87 1072 macro avg 0.87 0.8... | 0d9bd273a23761277847e8da0e7413f9 |
apache-2.0 | ['text-classification', 'generic'] | false | sk-40148507-8fa3-419e-8462-0cd31028ba20 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;} | 4694b085d7c9cd12b028fe85fd44b530 |
apache-2.0 | ['text-classification', 'generic'] | false | sk-40148507-8fa3-419e-8462-0cd31028ba20 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;} | 9d826cb59e94ffa0e51463ad63c07777 |
apache-2.0 | ['text-classification', 'generic'] | false | sk-40148507-8fa3-419e-8462-0cd31028ba20 div.sk-container {/* jupyter\'s `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org.... | 3ad6fe69724869a70a02a724d7c32b33 |
apache-2.0 | ['text-classification', 'generic'] | false | sk-40148507-8fa3-419e-8462-0cd31028ba20 div.sk-text-repr-fallback {display: none;}</style><div id="sk-40148507-8fa3-419e-8462-0cd31028ba20" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(& | 4183d544bbcd42fb054c90084bf117f1 |
apache-2.0 | ['text-classification', 'generic'] | false | x27;, LogisticRegression())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="904... | ded5aebea4580244837d4cc7deec9079 |
apache-2.0 | ['text-classification', 'generic'] | false | x27;, LogisticRegression())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="55bb4869-7378-430d-a174-0c343e24018c" type="checkbox" ><label for="55bb4869-7378-430d-a174-0c343e24018c" class="sk-toggle... | 1678b513f900c3c1c1e98c09bd41f47d |
apache-2.0 | ['text-classification', 'generic'] | false | x27;)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="c6377f91-830e-4547-9bf8-9d4f0aa2fb8c" type="checkbox" ><label for="c6377f91-830e-4547-9bf8-9d4f0aa2fb8c" class="sk-toggleable__label sk-toggleable__label-arrow">Logisti... | 224f2f853e9b126cfc3c4ca8e8863e88 |
apache-2.0 | ['text-classification', 'generic'] | false | Hyperparameters β€οΈ You can find hyperparameters below ππ»β¨ ``` {'memory': None, 'steps': [('embedding', HFTransformersLanguage(model_name_or_path='facebook/bart-base')), ('model', LogisticRegression())], 'verbose': False, 'embedding': HFTransformersLanguage(model_name_or_path='facebook/bart-base'), 'model': ... | 6c55413ddf9cecfb6bc659ec21a63cb6 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_One_100v5_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one100v5_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.4636 - Precision: 0.2791 - Recall: 0.2144 - F1: 0.2425 - Accura... | 3c40a22c86833ca4720dade8adc44b89 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 41 | 0.5040 | 0.2172 | 0.1266 | 0.1599 | 0.8226 | | No log | 2.0 |... | 17836c234e2946c91f66c2a01d0def8a |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-turkish-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: - Loss: 2.7642 - Wer: 0.5894 | 1e7b6595846c62c3b8463ab40697eaef |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 24.5372 | 9.76 | 400 | 5.2857 | 0.9738 | | 4.3812 | 19.51 | 800 | 3.6782 | 0.7315 | | 1.624 | 29.27 | 1200 | 2.7642 | 0.5894 | ... | 4df58bbf8e2a0d43bf7a6709b29e05f5 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Icelandic This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the samromur dataset. It achieves the following results on the evaluation set: - Loss: 0.2613 - Wer: 23.0409 | 3b9bcf75eb52f71ae7e3fc43788d83e0 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3551 | 0.18 | 1000 | 0.4322 | 35.0421 | | 0.2541 | 0.36 | 2000 | 0.3249 | 27.4721 | | 0.231 | 0.53 | 3000 | 0.2781 | 24.223... | 36734b77c0a145b581e60c1d4aa1eaa4 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr8e06-wd0.01-bs16 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2795 - Rmse: 0.5286 - Mse: 0.2795 - Mae: 0.4342 | 2d7993d116b56978d548e08b4e0aeb30 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-06 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 83efd68195f689f1513f84c6a1d29c25 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2767 | 1.0 | 1245 | 0.2745 | 0.5239 | 0.2745 | 0.4140 | | 0.2741 | 2.0 | 2490 | 0.2760 | 0.5254 | 0.2760 ... | 5bcd6b5fb34b0bbf123e91f56964e50d |
apache-2.0 | [] | false | Intended uses & limitations You can classify if the input tweet (or any others statement) about COVID-19/vaccine is `true`, `false` or `misleading`. Note that since this model was trained with data up to May 2020, the most recent information may not be reflected. | 563a6deb9d0016e90f0c07db843b4459 |
apache-2.0 | [] | false | How to use You can use this model directly on this page or using `transformers` in python. - Load pipeline and implement with input sequence ```python from transformers import pipeline pipe = pipeline("sentiment-analysis", model = "ans/vaccinating-covid-tweets") seq = "Vaccines to prevent SARS-CoV-2 infection are con... | ae0f7f6cc9651e1f7c6e5070769ff155 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.