license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 | 91fec614d907c8574f4d5bb3e68f917a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:| | 1.5664 | 3.78 | 5000 | 2.6110 ... | d822a10f2ef4be69cdfb751618f13c1e |
mit | ['autotrain', 'summarization'] | false | Model Description This model is an attempt to simplify code understanding by generating line by line explanation of a source code. This model was fine-tuned using the Salesforce/codet5-large model. Currently it is trained on a small subset of Python snippets. | 3e00ade90951fa8456fb6db15d9f09b5 |
mit | ['autotrain', 'summarization'] | false | Model Usage ```py from transformers import ( AutoModelForSeq2SeqLM, AutoTokenizer, AutoConfig, pipeline, ) model_name = "sagard21/python-code-explainer" tokenizer = AutoTokenizer.from_pretrained(model_name, padding=True) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) config = AutoConfig... | 1815489f24333d81c9680bedd3c98d30 |
apache-2.0 | ['t5-large', 'text2text-generation', 'conversational question rewriting'] | false | t5-large-coqr-canard This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the [CANARD](https://sites.google.com/view/qanta/projects/canard) dataset. It achieves the following results on the test set: - Loss: 0.3064 - Bleu: 77.1979 - Generation Length: 9.576 | ac79a9ba623039c145ce2bf35d6d6ccc |
apache-2.0 | ['t5-large', 'text2text-generation', 'conversational question rewriting'] | false | Model description CANARD dataset rewrites the original questions in conversations to make them context-independent (understandable w/o context). On the contrary, this model is trained to rewrite context-independent questions to conversational questions, aiming to create fluent dialog with anaphora and ellipsis. Inpu... | a9bd8d0f25760bcfe909d509bbde71c4 |
apache-2.0 | ['t5-large', 'text2text-generation', 'conversational question rewriting'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 512 - total_eval_batch_size: 512 - optimizer: Adafactor - lr_scheduler_type: linear ... | c968f09835a21f9d11c8dd86b69f7d1a |
apache-2.0 | ['t5-large', 'text2text-generation', 'conversational question rewriting'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 62 | 0.2987 | 77.2361 | 9.4534 | | 6ddb6bc2811c03fb91898edc041cafff |
mit | ['generated_from_trainer'] | false | indobert-base-p2-finetuned-mer This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.1964 | 0bf650d5b723b7b6e4a4333709e79ef1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.7183 | 1.0 | 28 | 6.6949 | | 6.3179 | 2.0 | 56 | 5.7267 | | 5.5857 | 3.0 | 84 | 5.2449 | | 5.17 | 4.0 | 112 | 4.8586 ... | 740dfae4a41643d20e5eec0069411696 |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-deletion-squad-15 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1057 | ff89f76205794b6819a20587db141e64 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | f52f5cd425798997faf31c67370c32b7 |
apache-2.0 | ['translation'] | false | opus-mt-guw-de * source languages: guw * target languages: de * OPUS readme: [guw-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/guw-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | 9220d0d862628d444bd9afde47f3a3bc |
mit | ['roberta-base', 'roberta-base-epoch_26'] | false | RoBERTa, Intermediate Checkpoint - Epoch 26 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | beb42d3974db5624b5ed0c2486438080 |
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: 0.2207 - Accuracy is: 0.9185 - F1: 0.9185 | 72bcca41a51881034550c80e5c7874dd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy is | F1 | |:-------------:|:-----:|:----:|:---------------:|:-----------:|:------:| | 0.8026 | 1.0 | 250 | 0.3114 | 0.905 | 0.9035 | | 0.2409 | 2.0 | 500 | 0.2207 | 0.9185 | 0.9185 | | 1350c4b79558f80df17922f95f317c95 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the fluffalpaca concept trained on the CCMat/db-aplaca dataset. This is a Stable Diffusion model fine-tuned on the fluffalpaca concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of fluffalpaca llama** This model was created as part of the DreamBooth Hackathon �... | eb166a7a6feb62b1da87c4d4446fbd34 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | Samples Prompt: "fluffalpaca llama in space by Enki Bilal"  Prompt: "fluffalpaca llama in front of the Eiffel Tower"  Prompt: "a photo of fluffalpaca llama swimming in the river" ![example i... | 2f46ee8017eaf4c51cd0c5e11d482773 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Model Dreambooth concept Bronya được train bởi tranmc bằng [Shinja Zero SoTA DreamBooth_Stable_Diffusion](https://colab.research.google.com/drive/1G7qx6M_S1PDDlsWIMdbZXwdZik6sUlEh) notebook <br> Test concept bằng [Shinja Zero no Notebook](https://colab.research.google.com/drive/1Hp1ZIjPbsZKlCtomJVmt2oX7733W44b0) <br... | 9c86125478c6e4aec0f71a28bacb1b87 |
mit | ['deberta', 'deberta-v3'] | false | What is this? This model has been developed to detect "narrative-style" jokes, stories and anecdotes (i.e. they are narrated as a story) spoken during speeches or conversations etc. It works best when jokes/anecdotes are at least 40 words or longer. It is based on [Moritz Laurer's DeBERTa-v3](https://huggingface.co/Mo... | fe3937186eff1aeb97ea214c6df77b92 |
mit | ['deberta', 'deberta-v3'] | false | How to use ```python from transformers import pipeline import torch device = 0 if torch.cuda.is_available() else -1 model_name = 'Reggie/DeBERTa-v3-base-joke_detector/' max_seq_len = 510 pipe = pipeline(model=model_name, device=device, truncation=True, max_length=max_seq_len) is_it_a_joke = """A nervous passenger is... | 080d0ba2230821f609053446fae15c36 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 48 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | ecaf13e1a686eaf64301c7c8f16406c6 |
apache-2.0 | ['translation'] | false | ccs-eng * source group: South Caucasian languages * target group: English * OPUS readme: [ccs-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ccs-eng/README.md) * model: transformer * source language(s): kat * target language(s): eng * model: transformer * pre-processing: normalization ... | 15a50ca1627df8f54cabb265ee7e267d |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ccs-eng - source_languages: ccs - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ccs-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ka', 'ccs', 'en'] - src_constituents: {'kat'} - tg... | 7e9a56429a71a8765805144f78453a8a |
mit | [] | false | Paul Noir on Stable Diffusion This is the `<paul-noir>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also t... | f5666aa44f1dfc252bd3eed582cf0701 |
cc-by-sa-4.0 | [] | false | Danish BERT for emotion classification The BERT Emotion model classifies a Danish text in one of the following class: * Glæde/Sindsro * Tillid/Accept * Forventning/Interrese * Overasket/Målløs * Vrede/Irritation * Foragt/Modvilje * Sorg/trist * Frygt/Bekymret It is based on the pretrained [Danish BERT](https://githu... | c573d18fc6c89fbb3b7be93422e6f52d |
cc-by-sa-4.0 | [] | false | bert-emotion) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForSequenceClassification model = BertForSequenceClassification.from_pretrained("alexandrainst/da-emotion-classification-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-emotion-clas... | 9c287e3b7d1db5d6f85e77b8bfb0dc16 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Persian (fa) 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](htt... | 2bcd76c0711ca8878744c69f81030a67 |
apache-2.0 | [] | false | BART (base-sized model) BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://gith... | b1bcb34f9eaf3c70b01f9a627ebb8b2b |
apache-2.0 | [] | false | Model description BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. BART is particularly eff... | 0e3da764bf3ec0f01e3878813a78e030 |
apache-2.0 | [] | false | Intended uses & limitations You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=bart) to look for fine-tuned versions on a task that interests you. | 77305b389ddb17fec482be4f6c5e698b |
apache-2.0 | [] | false | How to use Here is how to use this model in tf_transformers: ```python from tf_transformers.models import BartModel from transformers import BartTokenizer tokenizer = BartTokenizer.from_pretrained('facebook/bart-base') model = BartModel.from_pretrained('facebook/bart-base') inputs_tf = {} inputs = tokenizer("Hello... | ea8406eafcb3138ccf7008e1fee56f54 |
apache-2.0 | [] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1910-13461, author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov an... | f4c0add754c0c651deb50fd6b03340fa |
mit | ['tn', 'fill-mask', 'pytorch', 'roberta', 'masked-lm'] | false | How to use ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("jannesg/takalane_ssw_roberta") model = AutoModelWithLMHead.from_pretrained("jannesg/takalane_ssw_roberta") ``` | 41aa9c0436520aaf44a5542f3b5943d6 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0775 - Accuracy: 0.9730 | 54f2b82804f0c63593b7da071dcf4c31 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2658 | 1.0 | 190 | 0.1305 | 0.9615 | | 0.1591 | 2.0 | 380 | 0.0781 | 0.9726 | | 0.1364 | 3.0 | 570 | 0.0775 | 0.... | 863527a6dba24be16ac0de42388655d6 |
mit | ['roberta-base', 'roberta-base-epoch_70'] | false | RoBERTa, Intermediate Checkpoint - Epoch 70 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | 3c509dfa18b27049222c2cb20426e95f |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base_toy_train_data_random_noise_0.1 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: 0.9263 - Wer: 0.7213 | 325ebebd554b02444039191b5270eafa |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 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... | cfb88a1f9584829305f09ff66dcb3cf8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.1296 | 2.1 | 250 | 3.5088 | 1.0 | | 3.0728 | 4.2 | 500 | 3.1694 | 1.0 | | 1.8686 | 6.3 | 750 | 1.3414 | 0.9321 | |... | 4eea0c1d8396c1125d84cbb3012d7b10 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the svsv concept trained by EloimEssaim. This is a Stable Diffusion model fine-tuned on the svsv concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of svsv dog** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https:... | 2684ad94a9777087acb494dd1fbe37e1 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Cburnett-Helmet-Concept-2 Dreambooth model trained by Arsenalalex108 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/The... | 3de0cd7512ab025cce22826db9b9a097 |
mit | [] | false | Iridescent Illustration Style on Stable Diffusion This is the `<iridescent-illustration-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_i... | 8d583d6851637f9e8fdb81a084a51ec9 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_unispeech_s514 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) 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 that you... | b502ecee094dda76f96d72f987892e8d |
apache-2.0 | ['generated_from_trainer'] | false | Article_500v2_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v2_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2086 - Precision: 0.7113 - Recall: 0.7526 - F1: 0.7314 - Accuracy: 0.... | a1e6e6e26f9b3667093fdee0a50fc116 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 185 | 0.1795 | 0.6982 | 0.7530 | 0.7245 | 0.9412 | | No log | 2.0 |... | 07882a6d9a9af28125883b6d48491356 |
apache-2.0 | ['generated_from_trainer'] | false | bert-emotion This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.2994 - Precision: 0.7059 - Recall: 0.7093 - Fscore: 0.7066 | 059b9d2075fc501fcdec0f955159255b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 0.8638 | 1.0 | 815 | 0.6727 | 0.6987 | 0.6539 | 0.6706 | | 0.5072 | 2.0 | 1630 | 1.0434 | 0.7090 ... | a2c355409661db9a9e6aa2b86597f210 |
apache-2.0 | ['generated_from_trainer'] | false | bart-base-finetuned-squad This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.2399 | d60466a774dffd041b3573a7fc9d9b72 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.2 | eebf7abc3ed8625827e10839072cb65a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2393 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 - Accuracy: 1.0 | 01b5e3ff14de4cc7461cd387b902bd58 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 1 | 1.5491 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 2.0 | 2 | 1... | ad1c493bbb17ab49f037e5d0f3c8e79b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hindi', 'marathi'] | false | Wav2Vec2-Large-XLSR-53-Hindi-Marathi Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hindi and Marathi using the OpenSLR SLR64 datasets. When using this model, make sure that your speech input is sampled at 16kHz. | 6f4526c53bb02303d79520b5c09036a1 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hindi', 'marathi'] | false | Eval dataset: ```bash wget https://www.openslr.org/resources/103/Marathi_test.zip -P data/marathi unzip -P "K3[2?do9" data/marathi/Marathi_test.zip -d data/marathi/. tar -xzf data/marathi/Marathi_test.tar.gz -C data/marathi/. wget https://www.openslr.org/resources/103/Hindi_test.zip -P data/hindi unzip -P "w9I2{3B*"... | 615d45a942b68115f25e0b9e593ae60a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hindi', 'marathi'] | false | Usage The model can be used directly (without a language model) as follows, assuming you have a dataset with Marathi text and path fields: ```python import torch import torchaudio import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_... | 108ee64fb14d790e37c669c1f4cb89cd |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hindi', 'marathi'] | false | We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]) speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = speech_array[0].numpy() batch["sampling_rate"] = sampling_rate batch[... | 819237fba2c9e70fbbe112b14a49f174 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hindi', 'marathi'] | false | Code For Evaluation on OpenSLR (Hindi + Marathi : https://filebin.net/snrz6bt13usv8w2e/test_large.csv) ```python import torchaudio import torch import librosa import numpy as np import re test = Dataset.from_csv('test.csv') chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\... | 8e289baec2f401d9fc9dd2958c175838 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hindi', 'marathi'] | false | We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]) speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = speech_array[0].numpy() batch["sampling_rate"] = sampling_rate batch[... | 276954f103dffb97563035ef9339d6bb |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hindi', 'marathi'] | false | We need to read the audio 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 = tor... | 15585d5f8312dd1c400fa528892f2931 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hindi', 'marathi'] | false | we do not want to group tokens when computing the metrics batch["pred_strings"] = processor.batch_decode(pred_ids) return batch test = test.map(evaluate, batched=True, batch_size=32) print("WER: {:2f}".format(100 * wer.compute(predictions=test["pred_strings"], references=test["sentence"]))) ``` | 163a683f2898bc33da15a4e0cb8bacc5 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hindi', 'marathi'] | false | Code for Evaluation on Common Voice Hindi (Common voice does not have Marathi yet) ```python import torchaudio import torch import librosa import numpy as np import re from datasets import load_metric, load_dataset, Dataset from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC wer = load_metric("wer") processor ... | b16ca18834927d7d33422b03aa707acc |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hindi', 'marathi'] | false | We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]) speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = speech_array[0].numpy() batch["sampling_rate"] = sampling_rate batch[... | 7397a1b57f5133cd8e4c482c42133de1 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hindi', 'marathi'] | false | Run prediction on batch 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,... | c3d95aaeb4eb34c533b607f8ee7b5aef |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hindi', 'marathi'] | false | we do not want to group tokens when computing the metrics batch["pred_strings"] = processor.batch_decode(pred_ids) return batch test_data = load_dataset("common_voice", "hi", split="test") test_data = test_data.map(speech_file_to_array_fn) test_data = test_data.map(evaluate, batched=True, batch_size=... | 346b9001848b373068e8f1b01c080e0d |
mit | ['generated_from_trainer'] | false | T5-base fine-tuned on CUAD for Legal Contract Review (via QA) This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the cuad dataset. It achieves the following results on the evaluation set: - Loss: 0.2209 | b8c7d65445d106b989611455c29e71e2 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 10 | 5f8a3dda5fdc1f140217a39f3e8d7dfd |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.2809 | 1.0 | 2795 | 0.2331 | | 0.2459 | 2.0 | 5590 | 0.2253 | | 0.2355 | 3.0 | 8385 | 0.2220 | | 0.2212 | 4.0 | 11180 | 0.2203 ... | f3d2e6028b5f18e5e158dc1482f264a7 |
gpl-3.0 | ['conversational', 'gpt2'] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python import torch from transformers import AutoModelWithLMHead, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("erikycd/chatbot_hadita") model = AutoModelWithLMHead.from_pretrained("erikycd/chatbot_hadita") exit_c... | e10225b74875ee3b9adc5372b892ce50 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad-seed-9001 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.4060 | 88288362f2f46c117bb10e9722648c0b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2411 | 1.0 | 8235 | 1.2265 | | 0.9797 | 2.0 | 16470 | 1.2576 | | 0.791 | 3.0 | 24705 | 1.4060 | | db3c2af61049f07805612ddc882a638e |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_calendar-roberta-large-v1-4-93 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... | 3c103cf95ad55c674827ca6f335719de |
apache-2.0 | ['translation'] | false | opus-mt-sv-el * source languages: sv * target languages: el * OPUS readme: [sv-el](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-el/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 9cd8d4ea506c42c76671b3180621d10c |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-wikitext-target-conll2003 This model is a fine-tuned version of [muhtasham/small-mlm-wikitext](https://huggingface.co/muhtasham/small-mlm-wikitext) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1116 - Precision: 0.8899 - Recall: 0.9184 - F1: 0.9039 - Accuracy: 0.978... | 86e9f2e24031afca3b34af32b146879d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.223 | 1.14 | 500 | 0.0903 | 0.8418 | 0.8810 | 0.8609 | 0.9720 | | 0.0741 | 2.28 |... | e49d68f285cd55e556b364f8b0013845 |
mit | ['bart', 'id'] | false | Indonesia Recipe Ingredients Generator Model **WARNING: inference on Huggingface might not run since the tokenizer used is not transformers's tokenizer.** Feel free to test the model [in this space](https://huggingface.co/spaces/haryoaw/id-recigen) 😎 **Have fun on generating ingredients** 😎 This is a fine-tuned ... | 4473c1a441bad2457cac053068c604f2 |
mit | ['bart', 'id'] | false | Tokenizer Since we use `indobart-v2`, we need to use their tokenizer. First, install the tokenizer by doing `pip install indobenchmark-toolkit`. After that, you can load the tokenizer: ```python from indobenchmark.tokenization_indonlg import IndoNLGTokenizer tokenizer = IndoNLGTokenizer.from_pretrained("haryo... | cf5ff7a688f251da834e36dfc39639ed |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/clinic-kitchen_and_dining-roberta-domain-adaptation 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.... | 7995f1c714a02bf46ea9022ad5721b18 |
apache-2.0 | ['translation'] | false | eng-eus * source group: English * target group: Basque * OPUS readme: [eng-eus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-eus/README.md) * model: transformer-align * source language(s): eng * target language(s): eus * model: transformer-align * pre-processing: normalization + Sente... | 12f7f32c7344d913dd1fae154e13c3dc |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-eus - source_languages: eng - target_languages: eus - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-eus/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'eu'] - src_constituents: {'eng'} - tgt_const... | ddaa495cc3a2e215e3045d9a21d9b061 |
mit | ['generated_from_trainer'] | false | bert_base_tcm_0.5 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: 0.0149 - Criterio Julgamento Precision: 0.8409 - Criterio Julgamento Rec... | 0c7cb9c077ab6217ce64b0574564c212 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Criterio Julgamento Precision | Criterio Julgamento Recall | Criterio Julgamento F1 | Criterio Julgamento Number | Data Sessao Precision | Data Sessao Recall | Data Sessao F1 | Data Sessao Number | Modalidade Licitacao Precision | Modalidade Licitac... | a7c3aa5f62e7305d2638b9ca5770ecea |
mit | ['conversational'] | false | DialoGPT Trained on a customized various spiritual texts and mixed with various different character personalities. This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on the energy complex known as Ra. Some text has been changed from the original with the intent... | 3018c0426059ab1166eaf86c1c2c4ed6 |
apache-2.0 | ['generated_from_trainer'] | false | T5-model-1-feedback-0810 This model is a fine-tuned version of [theojolliffe/T5-model-1-feedback-0510](https://huggingface.co/theojolliffe/T5-model-1-feedback-0510) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1776 - Rouge1: 94.0404 - Rouge2: 91.0472 - Rougel: 93.8927 - Roug... | 30dda4c9fd59681ee547bc4ae90997b1 |
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 | 363 | 0.2000 | 93.0351 | 89.425 | 93.1359 | 93.2085 | 15... | 13129aa9af7422c03b54dbdf76ca2413 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xlsr-53-espeak-cv-ft-evn6-ntsema-colab This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2335 - Wer: 0.9431 | 72486af48db96562b9dd55deecb18fab |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.847 | 4.0 | 400 | 0.9836 | 0.9933 | | 0.8626 | 8.0 | 800 | 0.8241 | 0.9666 | | 0.536 | 12.0 | 1200 | 0.9166 | 0.9565 | |... | b7308cf0914f4cfc5d28586bcb0c36b0 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-mnli-target-glue-qqp This model is a fine-tuned version of [muhtasham/small-mlm-glue-mnli](https://huggingface.co/muhtasham/small-mlm-glue-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3263 - Accuracy: 0.8535 - F1: 0.8134 | 0da88f401ba1c985c4cc949f0bcf99c5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4778 | 0.04 | 500 | 0.4286 | 0.7863 | 0.7468 | | 0.4182 | 0.09 | 1000 | 0.3862 | 0.8142 | 0.7696 | | 0.4014 |... | 93d2727ca640e578936879a17dc2f6f0 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_play-roberta-large-v1-3-71 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 contras... | 2765efd4f17be01ac406dbd7f43ad3cb |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-v3-e16 This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9340 - Rouge1: 57.6388 - Rouge2: 44.834 - Rougel: 47.5043 - Rouge... | 0297a0cd2abcbf9d7e29eea7998307fe |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 - mixed_precision_training: Native AMP | 14931b273dbf0b64bcc9d93e461f71b2 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.2407 | 1.0 | 795 | 0.9270 | 53.3842 | 33.8559 | 35.7393 | 50.6907 |... | 553eaeb6d8ec2553018f4ddc884d6099 |
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: 0.2230 - Accuracy: 0.9265 - F1: 0.9265 | 90bfba814472d6f3b7f69971833a2626 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8356 | 1.0 | 250 | 0.3184 | 0.9055 | 0.9021 | | 0.2559 | 2.0 | 500 | 0.2230 | 0.9265 | 0.9265 | | 848154571a15abb4603bbbe648e266eb |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Japanese This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 ja dataset. It achieves the following results on the evaluation set: - Loss: 0.4317 - Wer: 13.3262 | de8d54b217cb481c15e8185440a21548 |
apache-2.0 | ['whisper-event', '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: constant_with_warmup - lr_scheduler_warmup_steps: 500 - training_steps: 10000 ... | 3f6c3d761c1e909ee6681939fb149406 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.042 | 3.03 | 2000 | 0.3056 | 12.9174 | | 0.0085 | 7.01 | 4000 | 0.3752 | 13.1746 | | 0.0047 | 10.04 | 6000 | 0.4103 | 1... | ec30b813c72edaf1ccc082ac6be743b1 |
apache-2.0 | ['generated_from_trainer'] | false | vit-convnext-tiny-224-eurosat This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0576 - Accuracy: 0.9859 | 69bfa2cfc76bb1ab9d686374711a5928 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2881 | 0.99 | 147 | 0.2325 | 0.9588 | | 0.0869 | 1.99 | 294 | 0.0912 | 0.9753 | | 0.0687 | 2.99 | 441 | 0.0663 | 0.... | 0509b9ab324f0441b4244002762663ad |
apache-2.0 | ['generated_from_trainer'] | false | gpt2-small-spanish-disco-poetry-15 This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/datificate/gpt2-small-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.2465 | 491458c54f422d5d358baff2128fa6d3 |
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