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 | [] | false | This model can encode 224x224 RGB image into 28x28x13bit (1274 bytes) latent. The compression rate is 28x28x13/(224x224x24)=1/118, or 0.203 bpp (same as VQGAN_f8_8192). 12M params for Encoder + Decoder. Trained on LAION-Aesthetics V2 5+ for 238M images. Now with 50M and 200M params checkpoints too :) Check the files... | d7f56784b780464f1c6689724dd09d8e |
mit | [] | false | Model description This model was inspired by -- and finetuned on the same dataset of -- [KoboldAI's GPT-Neo-125M-AID (Mia) model](https://huggingface.co/KoboldAI/GPT-Neo-125M-AID): the AI Dungeon dataset (`text_adventures.txt`). This was to fix a possible oversight in the original model, which was trained with [an unf... | e4bf498f535c9ee12f6d301a7db0a54e |
apache-2.0 | ['tapas', 'table-question-answering'] | false | TAPAS medium model fine-tuned on WikiTable Questions (WTQ) This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_medium_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was pre-trained on MLM a... | a3c969a91a1bbcb09c75843d2bee2943 |
apache-2.0 | ['tapas', 'table-question-answering'] | false | Results Size | Reset | Dev Accuracy | Link -------- | --------| -------- | ---- LARGE | noreset | 0.5062 | [tapas-large-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-large-finetuned-wtq/tree/no_reset) LARGE | reset | 0.5097 | [tapas-large-finetuned-wtq](https://huggingface.co... | 9e0e3ef6bfaaf3a5d68cc0ef062b6e5f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-sexist-epoch2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3185 - Accuracy: 0.8621 - F1: 0.8602 - Precision: 0.7545 - Recall: 0.6942 | f21d57f49ced91b648e059d1144bd31c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.4341 | 1.0 | 197 | 0.3349 | 0.8629 | 0.8579 | 0.7898 | 0.6419 | | 0.3014 | 2.0 |... | f4e7f27c8caa827b13e263a8e6479f9b |
mit | ['ukrainian'] | false | This is a smaller version of the [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) model with only Ukrainian and some English embeddings left. * The original model has 470M parameters, with 384M of them being input and output embeddings. * After shrinking the `sentencepiece` vocabulary from 250K to 31K (top 25K... | 8622be6f8ea80ee0f31118a7b7cb5f78 |
apache-2.0 | ['automatic-speech-recognition', 'ar'] | false | exp_w2v2t_ar_xlsr-53_s841 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech... | b96f6c77009007ed9ad26eb66419de73 |
mit | ['generated_from_trainer'] | false | bert-to-distilbert-NER This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 44.0386 - Precision: 0.0145 - Recall: 0.0185 - F1: 0.0163 - Accuracy: 0.7597 | 9e0e7c831fce38b66fe1c25d891d6fa1 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP | 33fb73f60fbcf3063136c7af8b8b1fbb |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 201.4012 | 1.0 | 110 | 133.7231 | 0.0153 | 0.0106 | 0.0125 | 0.7539 | | 106.9317 | 2.0 |... | 9d26143a0c861df9c5693d1449473e79 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_vp-es_s250 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-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 you... | 76cd2664fe25c5dfe8efb0fa17a6e50b |
apache-2.0 | [] | false | microsoft/layoutxlm-base finetuned on XFUND.ja Training Results { "epoch": 40.0, "eval_accuracy": 0.7919082377476538, "eval_f1": 0.7886944818304172, "eval_loss": 1.6934013366699219, "eval_mem_cpu_alloc_delta": 819200, "eval_mem_cpu_peaked_delta": 0, "eval_mem_gpu_alloc_delta": 0, "eval... | 007d4cc2c2248b7253cd8790fdf9c475 |
apache-2.0 | ['generated_from_trainer'] | false | results This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0002 - Accuracy: 0.8923 - F1: 0.9167 - Precision: 0.8462 - Recall: 1.0 | 707e331321a2bc2d0d71709188c393af |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.0026 | 1.0 | 1956 | 0.0003 | 0.9552 | 0.9636 | 0.9298 | 1.0 | | 0.0015 | 2.0 |... | e2751624d6176261789772ca7f3f69ea |
apache-2.0 | ['legal', 'spanish'] | false | Spanish Legal-domain RoBERTa There are few models trained for the Spanish language. Some of the models have been trained with a low resource, unclean corpora. The ones derived from the Spanish National Plan for Language Technologies are proficient solving several tasks and have been trained using large scale clean co... | 5bd2dbd85f40023223fb01cee249765e |
apache-2.0 | ['legal', 'spanish'] | false | Citing ``` @misc{gutierrezfandino2021legal, title={Spanish Legalese Language Model and Corpora}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Aitor Gonzalez-Agirre and Marta Villegas}, year={2021}, eprint={2110.12201}, archivePrefix={arXiv}, primaryClass={cs.CL} }... | 1c15ce24b1fd8bf7f942b64032805ce4 |
apache-2.0 | ['vision', 'image-classification'] | false | Van Van model trained on imagenet-1k. It was introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [this repository](https://github.com/Visual-Attention-Network/VAN-Classification). Disclaimer: The team releasing Van did not write a model card for this model so t... | 897513891c61ab59c50e117b4f414c89 |
apache-2.0 | ['vision', 'image-classification'] | false | Model description This paper introduces a new attention layer based on convolution operations able to capture both local and distant relationships. This is done by combining normal and large kernel convolution layers. The latter uses a dilated convolution to capture distant correlations.  to look for fine-tuned versions on a task that interests you. | 2a5b90eb1d5855e1dbc72a111bed1950 |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model: ```python >>> from transformers import AutoFeatureExtractor, VanForImageClassification >>> import torch >>> from datasets import load_dataset >>> dataset = load_dataset("huggingface/cats-image") >>> image = dataset["test"]["image"][0] >>> feature_extractor = AutoFeatureExt... | a1b3839abb20f8a87bf263b289f2807e |
apache-2.0 | ['vision', 'image-classification'] | false | model predicts one of the 1000 ImageNet classes >>> predicted_label = logits.argmax(-1).item() >>> print(model.config.id2label[predicted_label]) tabby, tabby cat ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/van). | b67dce00c5e81db7058035938b895b11 |
apache-2.0 | [] | false | bert-base-en-uk-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the ... | bf5903cffa2e987819122945dbef80f1 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-uk-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-uk-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](h... | 25af7a2f461f537107eb15bb38e71adb |
apache-2.0 | ['generated_from_trainer'] | false | muril-base-cased-finetuned-code-mixed-DS This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9319 - Accuracy: 0.6982 - Precision: 0.6327 - Recall: 0.6314 - F1: 0.6320 | 57fb2408313bc11a149ee3bc463dc616 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.0542 | 1.98 | 248 | 0.9786 | 0.5976 | 0.3936 | 0.5454 | 0.4330 | | 0.9307 | 3.97 |... | e37daca3cb70dfe311762a29ee5b1f3c |
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: 0.0665 - Precision: 0.9261 - Recall: 0.9455 - F1: 0.9357 - Accuracy: 0.9851 | bc804bb9c30bec4cd03fee5f5e8ec915 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0852 | 1.0 | 1756 | 0.0650 | 0.9197 | 0.9367 | 0.9281 | 0.9830 | | 0.0407 | 2.0 |... | c1a92d1ce9a1b70ff118b4235026b514 |
mit | [] | false | furrpopasthetic on Stable Diffusion This is the `<furpop>` 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 als... | caadd9b68eeff65d073748e46bcfd566 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-checkpoint-14 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-13](https://huggingface.co/jiobiala24/wav2vec2-base-checkpoint-13) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.2822 - Wer: 0.4068 | ef10854281f0a3e46631e33f0c91ba21 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.1996 | 1.59 | 1000 | 0.7181 | 0.4079 | | 0.1543 | 3.17 | 2000 | 0.7735 | 0.4113 | | 0.1171 | 4.76 | 3000 | 0.8152 | 0.404... | 79616c67013e459238323a490143b9d8 |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_unispeech-ml_s527 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using... | b21e9f7eb0d57af6a07d574224a536cb |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | DreamBooth model for the montserratbcn concept trained by rodo1985 on the rodo1985/montserrat_mountain_dataset dataset This is a Stable Diffusion model fine-tuned on `mountain` images for the landscape theme. It can be used by modifying the `instance_prompt`: **a photo of montserratbcn mountain** This model was crea... | b0e0497f79828033e8080accc05a7200 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Description Montserrat is a mountain range located near Barcelona, Spain. Montserrat mountain is known for its distinctive, jagged peaks, which are made of conglomerate rock and rise to an elevation of over 4,000 feet (1,200 meters) above sea level. The mountain's unique geology is a result of its location at the co... | 157145015f74ede0373d643245f3f2df |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Examples of generated images      | bac9c3553852fe5839b2a02e537c78cd |
apache-2.0 | ['generated_from_keras_callback'] | false | distilbert_oscarth_0100 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0777 - Validation Loss: 1.0396 - Epoch: 99 | 7e9844a00c772b6842240efc60170d3f |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.1327 | 2.9983 | 0 | | 2.7813 | 2.4562 | 1 | | 2.4194 | 2.2066 | 2 | | 2.2231 | 2.0562 | 3 | | 2.0894 | 1.9450 | 4 | | 1.9905 |... | cfd793ab28933f4436b85ba82c9279a9 |
apache-2.0 | ['generated_from_keras_callback'] | false | saketh-chervu/distilroberta-base-finetuned-distilroberta This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.1462 - Epoch: 0 | a4a0007a32bae0ec0cd2030c252d6d8b |
apache-2.0 | ['translation'] | false | opus-mt-bem-sv * source languages: bem * target languages: sv * OPUS readme: [bem-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bem-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | ff347d416bec98a65bdc96681b2a42d7 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-DM128 (Deep-Narrow version) T5-Efficient-SMALL-DM128 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoin... | db4fe6ef0e134772df0d4a798364f186 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-dm128** - is of model type **Small** with the following variations: - **dm** is **128** It has **15.14** million parameters and thus requires *ca.* **60.57 MB** of memory in full precision (*fp32*) or **30.28 MB** of memory in half precision (... | e5808fc6ea8a43c5d3dd513951996250 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | flat-icons Dreambooth model trained by viba98 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-dif... | b5b1bb459a69f375cf41e93e79957a3e |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-home-16-16-5-oos 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: 2.3789 - Accuracy: 0.3356 | dd957a4b1d67155303cb6c870e8c0dcf |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-credit_cards-7-16-5 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: 2.3376 - Accuracy: 0.3186 | 3436c12d0000500655102e4b517824ad |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Wav2Vec2 XLS-R 300M Cantonese (zh-HK) LM Wav2Vec2 XLS-R 300M Cantonese (zh-HK) LM is an automatic speech recognition model based on the [XLS-R](https://arxiv.org/abs/2111.09296) architecture. This model is a fine-tuned version of [Wav2Vec2-XLS-R-300M](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the `zh-HK... | ddeb62e1e6d7410600d916f0d0846c54 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | params | Arch. | Training/Validation data (text) | | --------------------------------- | ------- | ----- | ------------------------------- | | `wav2vec2-xls-r-300m-zh-HK-lm-v2` | 300M | XLS-R | `Common Voice zh-HK` Dataset | | 1e341447c0c2d0fd7943b74b1ee56b05 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Evaluation Results The model achieves the following results on evaluation without a language model: | Dataset | CER | | -------------------------------- | ------ | | `Common Voice` | 31.73% | | `Common Voice 7` | 23.11% | | `Common Voice 8` ... | 8ef07ece01d83ec265630bad8f79d8a9 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training procedure The training process did not involve the addition of a language model. The following results were simply lifted from the original automatic speech recognition [model training](https://huggingface.co/w11wo/wav2vec2-xls-r-300m-zh-HK-v2). | b88f2094c646db737aba3fc8e02ac623 |
apache-2.0 | ['generated_from_trainer'] | false | Bert-test-model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.3708 | 0317b51cdbfa0e88c228a39724bacb9d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 250 | 1.7369 | | 2.2639 | 2.0 | 500 | 1.3940 | | 2.2639 | 3.0 | 750 | 1.3708 | | 8ed20e58ccc768d539576d62d56f8efd |
apache-2.0 | ['generated_from_trainer'] | false | openai/whisper-small 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: 0.3747 - Wer: 14.8058 | 0b870c9f96b6235a6563cd7df625371d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2803 | 1.0 | 1000 | 0.3747 | 14.8058 | | 75e968a0c6d412b367498f4329157fb4 |
apache-2.0 | ['sequence-classification', 'int8'] | false | Quantized BERT-base MNLI model with 90% of usntructured sparsity The pruned and quantized model in the OpenVINO IR. The pruned model was taken from this [source](https://huggingface.co/neuralmagic/oBERT-12-downstream-pruned-unstructured-90-mnli) and quantized with the code below using HF Optimum for OpenVINO: ```p... | dcd072ede61c50ce0951d60b51eda64d |
apache-2.0 | ['sequence-classification', 'int8'] | false | "typeform/distilbert-base-uncased-mnli" model = AutoModelForSequenceClassification.from_pretrained(model_id) tokenizer = AutoTokenizer.from_pretrained(model_id) save_dir = "./nm_mnli_90" def preprocess_function(examples, tokenizer): return tokenizer(examples["premise"], examples["hypothesis"], padding="max_length... | af351f0ba75b0a063044d37c99dcc4fc |
apache-2.0 | ['sequence-classification', 'int8'] | false | Create the calibration dataset used to perform static quantization calibration_dataset = quantizer.get_calibration_dataset( "glue", dataset_config_name="mnli", preprocess_function=partial(preprocess_function, tokenizer=tokenizer), num_samples=100, dataset_split="train", ) | d341799bb8f78f131140acfc477529d2 |
apache-2.0 | ['sequence-classification', 'int8'] | false | Apply static quantization and export the resulting quantized model to OpenVINO IR format quantizer.quantize( quantization_config=quantization_config, calibration_dataset=calibration_dataset, save_directory=save_dir, ) | 5a54f10e2ca083084c5146381e7b07a1 |
apache-2.0 | ['generated_from_keras_callback'] | false | ClaireV/MLMA_5.3 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: 0.0246 - Validation Loss: 0.0578 - Epoch: 2 | 131ded19ba2ad8ffd34cc114f5f72865 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1286 | 0.0630 | 0 | | 0.0401 | 0.0560 | 1 | | 0.0246 | 0.0578 | 2 | | 3e19fef31fd3891da86252ae86df1361 |
mit | [] | false | **Hyperparameters:** - learning rate: 2e-5 - weight decay: 0.01 - per_device_train_batch_size: 16 - per_device_eval_batch_size: 16 - gradient_accumulation_steps:1 - eval steps: 5000 - max_length: 128 - num_epochs: 3 **Dataset version:** - “craffel/tasky_or_not”, “10xp3_10xc4”, “15f88c8” **Checkpoint:** ... | 83db16e20e0aa38e2f160d2a91fdc848 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0626 - Precision: 0.9201 - Recall: 0.9350 - F1: 0.9275 - Accuracy: 0.9832 | 2ad3b7c4e037b1784c2ba0a04d1a18e1 |
apache-2.0 | ['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 - distributed_type: IPU - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - total_eval_batch_size: 5 - optimizer: Adam with betas=(0.9,0.999) and ... | ecb51942e50980245fd0e927e98b60c0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0943 | 1.0 | 877 | 0.0687 | 0.9019 | 0.9149 | 0.9084 | 0.9801 | | 0.2395 | 2.0 |... | f93bc452b9089d52b1c5c87b17409da4 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-jm-finetuned-panx-fr_hub 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: 0.2728 - F1: 0.8415 | f172d78d63cc054bfbf28ad8efe93e92 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5731 | 1.0 | 191 | 0.3237 | 0.7862 | | 0.2662 | 2.0 | 382 | 0.2680 | 0.8272 | | 0.1724 | 3.0 | 573 | 0.2728 | 0.8415 | ... | 92aac3a6ddb9725bcf982e5871f0e73a |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout beb62bd85e29b998bc10a65136f6cf4f6deebe70 pip install -e . cd egs2/aphasiabank/asr1 ./run.sh --skip_data_prep false --skip_train... | 9c9d1b6f0d0f50f3efb5e40c606c57a8 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Thu Jan 26 22:50:03 EST 2023` - python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.8.1` - Git hash: `beb62bd85e29b998bc10a65136f6cf4f6deebe70` - Commit date: `Thu Jan 19 23:52:08 2023 -0500` | e9eb5b2baa76e5fe434fcb1daaa2d0da |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_ebranchformer_small_wavlm_large.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_ebranchformer_small_wavlm_large_raw_en_char_sp ngpu: 1 seed: 2022 num_workers: 4 num_att_plot: ... | 6688e5be0a135e0e1b9385bb84de2087 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | kodaka-saki-(Extreme-hearts) Dreambooth model trained by alea31415 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Prompt with "kodakasaki" Training details: - data set: 47 concept images (partially tagged) + 43 c... | 89100f510f503f6332c96fb574a34a4a |
mit | ['generated_from_trainer'] | false | xlm-roberta-base_squad This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. - "eval_exact_match": 82.69631031220435 - "eval_f1": 89.4562841806503 - "eval_samples": 10918 | 3651e53890cfe22fef013cac4bedf250 |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.8522 | 63379c77edf1fdc2bdc3798f3ccfbe24 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 250 | 2.6732 | | 2.8375 | 2.0 | 500 | 1.9453 | | 2.8375 | 3.0 | 750 | 1.8522 | | 69b88ee425c5c51e7570dda1642bf3b6 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-bert-mlm This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8491 | c42bfd8c09af0fd2cf824f6251f9b599 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2439 | 1.0 | 208 | 1.9789 | | 2.008 | 2.0 | 416 | 1.8568 | | 1.9535 | 3.0 | 624 | 1.8443 | | a5d20a4ccc3b7b1373d80a23f0d484bc |
apache-2.0 | ['generated_from_keras_callback'] | false | Simon10/simone-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3024 - Validation Loss: 0.1714 - Epoch: 0 | b2bd193cebe8223a6e556f9c9d5681a7 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | 54d99b343e010de2d9e83cfde64919b9 |
mit | ['generated_from_trainer'] | false | deberta-classifier-feedback-1024-pseudo This model is a fine-tuned version of [TTian/deberta-classifier-feedback-1024](https://huggingface.co/TTian/deberta-classifier-feedback-1024) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1018 | 52a809b7b5f1d5a16ed4c6d6491adc9d |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoc... | 09924d67ab8a1b0caa5a54eb93d1ab63 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5566 | 0.01 | 10 | 0.8514 | | 0.3244 | 0.03 | 20 | 0.8647 | | 0.2631 | 0.04 | 30 | 0.6700 | | 0.2237 | 0.06 | 40 | 0.8904 ... | cbfe913f5a6e42270067c5591fae403b |
apache-2.0 | ['generated_from_keras_callback'] | false | hamishm/distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7763 - Validation Loss: 1.1324 - Epoch: 1 | 7c3cf92504f8263b2b4e996ce04c2541 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | fnet-base-finetuned-mnli This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6443 - Accuracy: 0.7675 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co... | d5eaa4eb23a8e6e66152bba7b6ac5ee6 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | !/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name mnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-mnli \\n --push_to_hub \\n --h... | 48f341a3616efedd370f956059f67612 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.7143 | 1.0 | 24544 | 0.6169 | 0.7504 | | 0.5407 | 2.0 | 49088 | 0.6218 | 0.7627 | | 0.4178 | 3.0 | 73632 | 0.6564 ... | f06c47650e53f7bf115db9ff4b3a315f |
apache-2.0 | ['generated_from_trainer'] | false | Bert_Classifier This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 2.8684 - Accuracy: 0.5133 | 7c1b5fd69e73abdc5a82c6e03339e820 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 188 | 2.3115 | 0.5267 | | No log | 2.0 | 376 | 2.5268 | 0.5467 | | 0.2313 | 3.0 | 564 | 2.8684 | 0.... | c3ec64cd9c6cb7ac22151dd6f2bac125 |
apache-2.0 | ['generated_from_keras_callback'] | false | Haakf/allsides_left_text_conc_overfit This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.1439 - Validation Loss: 2.1218 - Epoch: 19 | ac686062a48cbf39e803501210250b63 |
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... | c4d48e2d882fa09ae241408286af7ed6 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.2490 | 2.1965 | 0 | | 2.2323 | 2.1616 | 1 | | 2.2201 | 2.1250 | 2 | | 2.1866 | 2.1199 | 3 | | 2.1734 | 2.1345 | 4 | | 2.1677 |... | a28b0187223f68b0fbcfd573b24bec27 |
apache-2.0 | ['translation'] | false | Usage example ```python from transformers import AutoTokenizer from optimum.onnxruntime import ORTModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("icon-it-tdtu/mt-en-vi-optimum") model = ORTModelForSeq2SeqLM.from_pretrained("icon-it-tdtu/mt-en-vi-optimum") text = "I am a student." inputs = tokenizer(text... | d208864b36411350cfb8ac26ee59e3d4 |
apache-2.0 | ['automatic-speech-recognition', 'fa'] | false | exp_w2v2t_fa_vp-fr_s198 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | f80221ee99169b92ce5057e83a8c082c |
apache-2.0 | ['translation'] | false | opus-mt-es-yo * source languages: es * target languages: yo * OPUS readme: [es-yo](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-yo/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 766c047f1f1742ce44c258c966d8dc89 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-arxiv This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1556 - Rouge1: 37.8405 - Rouge2: 20.4483 - Rougel: 33.996 - Rougelsum: 34.0071 - Gen Len: 15.8214 | 86b77348722ef877719f1bf0e7aa2838 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:---------:|:-------:| | 2.3825 | 1.0 | 3564 | 2.1556 | 37.8405 | 20.4483 | 33.996 | 34.0071 | 15.82... | 963013e0d84b5f23a842ef1e7c229808 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2_timit 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: 3.0791 - Wer: 1.0 | 2d685008d80e7d902100db1864d40296 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 | 20c3467f535ca25246cc6da1e7c876cc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 3.1506 | 2.4 | 300 | 3.1294 | 1.0 | | 3.0957 | 4.8 | 600 | 3.0791 | 1.0 | | fcceaae622d12e523990368134eddd1d |
mit | ['generated_from_keras_callback'] | false | sachinsahu/IPod-clustered This model is a fine-tuned version of [nandysoham16/15-clustered_aug](https://huggingface.co/nandysoham16/15-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4611 - Train End Logits Accuracy: 0.8715 - Train Start Logits Accuracy: ... | f4838af9d6b75c3ef4924f42a6e93c0b |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 59e88c5a3f6e5793a08b1c6d9bce4633 |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_unispeech-sat_s377 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 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your spee... | 323f75ef9cffd954ad9736f9fd6b3b5e |
openrail | [] | false | PVC Refurbished Little is known about this model from myself, only that it was created by an author who will go by the name "L". I have no useful information to offer, only my song and praise to L for this wonderful achievement. | 2b85e249f957fa5511b31b68943c4d58 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_itr0_3e-05_all_16_02_2022-20_27_36 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 None dataset. It achieves the following results on the evaluation set: - Loss: 0.1633 - Precision: 0... | 241de813ede164abaa40cf7c77b0efcf |
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