license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-13 * source language(s): heb * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-eng/opusTCv20210807+bt_transformer-big_2022-03-13.zip) * more information released models: [OPUS-MT heb-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-eng/README.md) | 94894311d6e4ab7ae2e4f31a34cc5134 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "היא שכחה לכתוב לו.", "אני רוצה לדעת מיד כשמשהו יקרה." ] model_name = "pytorch-models/opus-mt-tc-big-he-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) | 3318c3c817b40c31f676a9227c268774 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | I want to know as soon as something happens. ``` 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-he-en") print(pipe("היא שכחה לכתוב לו.")) | f8b8422d8f75a7384ba18d1cfc0d3f03 |
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/heb-eng/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-models/heb-eng/opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | | d2abc7e77f49c47451d5ad5f365ccdb0 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | heb-eng | tatoeba-test-v2021-08-07 | 0.68565 | 53.8 | 10519 | 77427 | | heb-eng | flores101-devtest | 0.68116 | 44.1 | 1012 | 24721 | | beb9017ac2c985a38677237ee7c31c69 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-utility-3-16-5 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: 2.3728 - Accuracy: 0.3956 | 921edc8c8050391755191cd8a179d315 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8194 | 1.0 | 1 | 2.6027 | 0.3156 | | 2.2337 | 2.0 | 2 | 2.5079 | 0.3778 | | 1.7996 | 3.0 | 3 | 2.4293 | 0.3822 | | 1.4591 | 4.0 | 4 | 2.3728 | 0.3956 | | 1.3205 | 5.0 | 5 | 2.3439 | 0.3956 | | 9c240ef7ab885e4fb2852ddc01f4f6d7 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-DL8 (Deep-Narrow version) T5-Efficient-BASE-DL8 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* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. | 0a1cfb701d19227fbf7c58a6a821e7fc |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-dl8** - is of model type **Base** with the following variations: - **dl** is **8** It has **185.17** million parameters and thus requires *ca.* **740.67 MB** of memory in full precision (*fp32*) or **370.34 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | | 10c1576c98e1d33d8ef02efecaae2c4d |
other | [] | false | Description NB: this version of the model is the improved version of [EIStakovskii/french_toxicity_classifier_plus](https://huggingface.co/EIStakovskii/french_toxicity_classifier_plus). To see the source code of training and the data please follow [the github link](https://github.com/eistakovskii/NLP_projects/tree/main/TEXT_CLASSIFICATION/data/Toxicity_Classifiers/DE_FR). This model was trained for toxicity labeling. The model was fine-tuned based off [the CamemBERT language model](https://huggingface.co/camembert-base). To use the model: ```python from transformers import pipeline classifier = pipeline("text-classification", model = 'EIStakovskii/french_toxicity_classifier_plus_v2') print(classifier("Foutez le camp d'ici!")) ``` | 56863738ea2d5425a1cc6a9938660026 |
other | [] | false | Comparison against Perspective This model was compared against the Google's [Perspective API](https://developers.perspectiveapi.com/s/?language=en_US) that similarly detects toxicity. Two models were tested on two datasets: the size of [200 sentences](https://github.com/eistakovskii/NLP_projects/blob/main/TEXT_CLASSIFICATION/data/Toxicity_Classifiers/DE_FR/test/test_fr_200.csv) and [400 sentences](https://github.com/eistakovskii/NLP_projects/blob/main/TEXT_CLASSIFICATION/data/Toxicity_Classifiers/DE_FR/test/test_fr_400.csv). The first one (arguably harder) was collected from the sentences of the [JigSaw](https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification/data) and [DeTox](https://github.com/hdaSprachtechnologie/detox) datasets. The second one (easier) was collected from the combination of sources: both from JigSaw and DeTox as well as [Paradetox](https://github.com/s-nlp/multilingual_detox/tree/main/data) translations and sentences extracted from [Reverso Context](https://context.reverso.net/translation/) by keywords. | c9e65be0ddc4b6034e6acad9b90ea332 |
other | [] | false | Perspective size|accuracy|f1 -|-|- 200|0.826|0.795 **400|0.632|0.418 **I suspect that Perspective has such a low score in the case of the FR dataset (400) because it refuses to trigger on the words "merde" and "putain" and some more rarer words in French like "cul" and so on. | e5d7a3dd1c5630aee513fa0d2b49f1e0 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-NL16 (Deep-Narrow version) T5-Efficient-SMALL-NL16 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* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. | ca1829fe89b57a9ca7afeea36ceea9bc |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-nl16** - is of model type **Small** with the following variations: - **nl** is **16** It has **133.97** million parameters and thus requires *ca.* **535.88 MB** of memory in full precision (*fp32*) or **267.94 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | | 1b57ae14f649322671e28658a1832434 |
mit | ['generated_from_trainer'] | false | lilt-en-funsd This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set: - Loss: 1.7928 - Answer: {'precision': 0.8716763005780347, 'recall': 0.9228886168910648, 'f1': 0.8965517241379309, 'number': 817} - Header: {'precision': 0.5648148148148148, 'recall': 0.5126050420168067, 'f1': 0.5374449339207047, 'number': 119} - Question: {'precision': 0.8945454545454545, 'recall': 0.9136490250696379, 'f1': 0.9039963252181902, 'number': 1077} - Overall Precision: 0.8678 - Overall Recall: 0.8937 - Overall F1: 0.8806 - Overall Accuracy: 0.7985 | b4ebabd0c0151a9425c8d8e1ed6a3759 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2500 - mixed_precision_training: Native AMP | 64d21d7e0d83c74d9ed61db817a12a96 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.4236 | 10.53 | 200 | 0.9583 | {'precision': 0.8623962040332147, 'recall': 0.8898408812729498, 'f1': 0.8759036144578314, 'number': 817} | {'precision': 0.5131578947368421, 'recall': 0.3277310924369748, 'f1': 0.39999999999999997, 'number': 119} | {'precision': 0.8450704225352113, 'recall': 0.947075208913649, 'f1': 0.893169877408056, 'number': 1077} | 0.8401 | 0.8872 | 0.8630 | 0.8016 | | 0.0421 | 21.05 | 400 | 1.4064 | {'precision': 0.8573113207547169, 'recall': 0.8898408812729498, 'f1': 0.8732732732732732, 'number': 817} | {'precision': 0.4301675977653631, 'recall': 0.6470588235294118, 'f1': 0.5167785234899329, 'number': 119} | {'precision': 0.8667883211678832, 'recall': 0.8820798514391829, 'f1': 0.87436723423838, 'number': 1077} | 0.8262 | 0.8713 | 0.8482 | 0.7733 | | 0.0121 | 31.58 | 600 | 1.5114 | {'precision': 0.8534090909090909, 'recall': 0.9192166462668299, 'f1': 0.8850913376546846, 'number': 817} | {'precision': 0.5930232558139535, 'recall': 0.42857142857142855, 'f1': 0.4975609756097561, 'number': 119} | {'precision': 0.8824577025823687, 'recall': 0.9201485608170845, 'f1': 0.9009090909090909, 'number': 1077} | 0.8583 | 0.8907 | 0.8742 | 0.8044 | | 0.0058 | 42.11 | 800 | 1.4988 | {'precision': 0.8361391694725028, 'recall': 0.9118727050183598, 'f1': 0.8723653395784543, 'number': 817} | {'precision': 0.5203252032520326, 'recall': 0.5378151260504201, 'f1': 0.5289256198347108, 'number': 119} | {'precision': 0.8798206278026905, 'recall': 0.9108635097493036, 'f1': 0.8950729927007299, 'number': 1077} | 0.8408 | 0.8892 | 0.8643 | 0.7982 | | 0.004 | 52.63 | 1000 | 1.5823 | {'precision': 0.8455467869222097, 'recall': 0.9179926560587516, 'f1': 0.880281690140845, 'number': 817} | {'precision': 0.5263157894736842, 'recall': 0.5042016806722689, 'f1': 0.5150214592274679, 'number': 119} | {'precision': 0.867595818815331, 'recall': 0.924791086350975, 'f1': 0.8952808988764045, 'number': 1077} | 0.8404 | 0.8972 | 0.8679 | 0.7996 | | 0.0028 | 63.16 | 1200 | 1.6518 | {'precision': 0.8492822966507177, 'recall': 0.8690330477356181, 'f1': 0.8590441621294616, 'number': 817} | {'precision': 0.5855855855855856, 'recall': 0.5462184873949579, 'f1': 0.5652173913043478, 'number': 119} | {'precision': 0.88, 'recall': 0.9192200557103064, 'f1': 0.899182561307902, 'number': 1077} | 0.8518 | 0.8768 | 0.8641 | 0.7939 | | 0.0013 | 73.68 | 1400 | 1.8819 | {'precision': 0.8378672470076169, 'recall': 0.9424724602203183, 'f1': 0.8870967741935485, 'number': 817} | {'precision': 0.6794871794871795, 'recall': 0.44537815126050423, 'f1': 0.5380710659898478, 'number': 119} | {'precision': 0.9006622516556292, 'recall': 0.8839368616527391, 'f1': 0.8922211808809747, 'number': 1077} | 0.8642 | 0.8818 | 0.8729 | 0.7931 | | 0.0013 | 84.21 | 1600 | 1.8234 | {'precision': 0.8519362186788155, 'recall': 0.9155446756425949, 'f1': 0.8825958702064898, 'number': 817} | {'precision': 0.5585585585585585, 'recall': 0.5210084033613446, 'f1': 0.5391304347826087, 'number': 119} | {'precision': 0.9120982986767486, 'recall': 0.8960074280408542, 'f1': 0.9039812646370023, 'number': 1077} | 0.8671 | 0.8818 | 0.8744 | 0.7996 | | 0.0008 | 94.74 | 1800 | 1.7898 | {'precision': 0.844170403587444, 'recall': 0.9216646266829865, 'f1': 0.8812170860152135, 'number': 817} | {'precision': 0.5294117647058824, 'recall': 0.5294117647058824, 'f1': 0.5294117647058824, 'number': 119} | {'precision': 0.8756613756613757, 'recall': 0.9220055710306406, 'f1': 0.898236092265943, 'number': 1077} | 0.8434 | 0.8987 | 0.8701 | 0.7901 | | 0.0004 | 105.26 | 2000 | 1.8115 | {'precision': 0.8396436525612472, 'recall': 0.9228886168910648, 'f1': 0.8793002915451895, 'number': 817} | {'precision': 0.6063829787234043, 'recall': 0.4789915966386555, 'f1': 0.5352112676056338, 'number': 119} | {'precision': 0.8909090909090909, 'recall': 0.9099350046425255, 'f1': 0.90032154340836, 'number': 1077} | 0.8561 | 0.8897 | 0.8726 | 0.7939 | | 0.0004 | 115.79 | 2200 | 1.7928 | {'precision': 0.8716763005780347, 'recall': 0.9228886168910648, 'f1': 0.8965517241379309, 'number': 817} | {'precision': 0.5648148148148148, 'recall': 0.5126050420168067, 'f1': 0.5374449339207047, 'number': 119} | {'precision': 0.8945454545454545, 'recall': 0.9136490250696379, 'f1': 0.9039963252181902, 'number': 1077} | 0.8678 | 0.8937 | 0.8806 | 0.7985 | | 0.0003 | 126.32 | 2400 | 1.8271 | {'precision': 0.863013698630137, 'recall': 0.9253365973072215, 'f1': 0.8930891907855877, 'number': 817} | {'precision': 0.6105263157894737, 'recall': 0.48739495798319327, 'f1': 0.5420560747663552, 'number': 119} | {'precision': 0.8935395814376706, 'recall': 0.9117920148560817, 'f1': 0.9025735294117648, 'number': 1077} | 0.8676 | 0.8922 | 0.8797 | 0.7983 | | 92372a121eb5d21e2a8175bd4cefe7b2 |
mit | [] | false | Gim on Stable Diffusion This is the `<grimes-album-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_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:         | 29001ae8ee0f75f435ba64c63c85149b |
mit | ['text-classification'] | false | Multi2ConvAI-Logistics: finetuned Bert for Polish
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Logistics (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: Polish (pl)
- model type: finetuned Bert
| 54d15cd548c3cdd3f2ded3f3a53961ba |
mit | ['text-classification'] | false | Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-logistics-pl-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-logistics-pl-bert")
````
| 7e273ec1b8d489b403f61ad30bb18fc6 |
apache-2.0 | ['generated_from_trainer'] | false | new_asr_model This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0553 - Wer: 0.1515 | d8ca6ca8850cca0cb1a7999b3c828828 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 36 - 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: 25 | cc7530fb1c56038a1b89e4b07089aa66 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 7.1498 | 3.88 | 500 | 1.9949 | 0.9938 | | 0.4835 | 7.75 | 1000 | 0.0690 | 0.1562 | | 0.1202 | 11.63 | 1500 | 0.0555 | 0.1513 | | 0.0842 | 15.5 | 2000 | 0.0564 | 0.1516 | | 0.0637 | 19.38 | 2500 | 0.0559 | 0.1521 | | 0.0647 | 23.26 | 3000 | 0.0553 | 0.1515 | | 98ad88bf028a6a55405b445942d0ef2f |
apache-2.0 | ['speech'] | false | SEW-small [SEW by ASAPP Research](https://github.com/asappresearch/sew) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc... Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi **Abstract** This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes. The original model can be found under https://github.com/asappresearch/sew | 105c95f3db068cc8e028ecdc304fc996 |
apache-2.0 | ['speech'] | false | Usage See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `SEWForCTC`. | 697a7bfa0f1ef0c2f7f8b430bff80cb5 |
mit | [] | false | DenseNet169 model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef). To use this model in Julia, [add the Metalhead.jl package to your environment](https://pkgdocs.julialang.org/v1/managing-packages/ | 8598b75981fb537f7505f96d9b917bfa |
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: 0.0814 | 2d45c07c2c305f59b50af8544b6e0c61 |
apache-2.0 | ['generated_from_trainer'] | false | Model description bert-base-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). Specifically, this model is a bert-base-cased model that was fine-tuned on the English version of the standard CoNLL-2003 Named Entity Recognition dataset. If you'd like to use a larger BERT-large model fine-tuned on the same dataset, a bert-large-NER version is also available. | 50470499c4e6353b1cabaf24a4be49ee |
apache-2.0 | ['generated_from_trainer'] | false | How to Use You can use this model with Transformers pipeline for NER. from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Hatman/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("Hatman/bert-finetuned-ner") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "My name is Wolfgang and I live in Berlin" ner_results = nlp(example) print(ner_results) | e1f396b1c2871c5baa3bd7bffa8b35a5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0181 | 1.0 | 1756 | 0.1301 | | 0.0166 | 2.0 | 3512 | 0.0762 | | 0.0064 | 3.0 | 5268 | 0.0814 | | fb6a02de5bfe5ddbc54266f35506ae42 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_0k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 3, Step 0k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model | dd3004d16110a795524c2506e4224a16 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_0k'] | 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_0k') model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_0k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_0k') model = BertModel.from_pretrained("google/multiberts-seed_3-step_0k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | a1487439af5473dcdad268175c38377c |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr 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: 0.1615 - F1: 0.8597 | 94fd22b4aec437ccd7a10721961bf384 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2876 | 1.0 | 715 | 0.1877 | 0.8274 | | 0.1472 | 2.0 | 1430 | 0.1573 | 0.8508 | | 0.0951 | 3.0 | 2145 | 0.1615 | 0.8597 | | 54574140f6f8b62a68d2479ea3b854a7 |
apache-2.0 | ['generated_from_trainer'] | false | bart-base-finetuned-xlsum-concat-multi-news This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8355 - Rouge1: 36.5801 - Rouge2: 15.2796 - Rougel: 29.8088 - Rougelsum: 29.8631 - Gen Len: 19.5457 | f7ec876a97e93dad08f437afc82b9827 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.6985 | 1.0 | 20543 | 1.8355 | 36.5801 | 15.2796 | 29.8088 | 29.8631 | 19.5457 | | 3f21438f206a2208d5da297ddfe315fc |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | an2-stable-diffusion Dreambooth model trained by Ryosuke 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-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: 00074-3537062306-portrait  | 5d99c5b0f7fe14cbd3f8eadfbb9c003d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion-ch02 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: 0.1703 - Accuracy: 0.934 - F1: 0.9342 | e930e4908845e26c6a9d41347b54b822 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2923 | 1.0 | 250 | 0.2001 | 0.9275 | 0.9263 | | 0.1485 | 2.0 | 500 | 0.1703 | 0.934 | 0.9342 | | 9e5b408522f0476e1c1195f10422c432 |
apache-2.0 | ['generated_from_trainer'] | false | bert-tiny-emotion-KD-distilBERT This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.5444 - Accuracy: 0.913 | 549859c44420afd1c0de233d5b67fa6a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 4.2533 | 1.0 | 1000 | 2.8358 | 0.7675 | | 2.3274 | 2.0 | 2000 | 1.5893 | 0.8675 | | 1.3974 | 3.0 | 3000 | 1.0286 | 0.891 | | 0.9035 | 4.0 | 4000 | 0.7534 | 0.8955 | | 0.6619 | 5.0 | 5000 | 0.6350 | 0.905 | | 0.5482 | 6.0 | 6000 | 0.6180 | 0.899 | | 0.4937 | 7.0 | 7000 | 0.5448 | 0.91 | | 0.4013 | 8.0 | 8000 | 0.5493 | 0.906 | | 0.3839 | 9.0 | 9000 | 0.5481 | 0.9095 | | 0.3281 | 10.0 | 10000 | 0.5528 | 0.9115 | | 0.3098 | 11.0 | 11000 | 0.5864 | 0.9095 | | 0.2762 | 12.0 | 12000 | 0.5566 | 0.9095 | | 0.2467 | 13.0 | 13000 | 0.5444 | 0.913 | | 0.2286 | 14.0 | 14000 | 0.5306 | 0.912 | | 0.2215 | 15.0 | 15000 | 0.5312 | 0.9115 | | 0.2038 | 16.0 | 16000 | 0.5242 | 0.912 | | 1607ff7a3fe085d08b6f262080751e5e |
mit | ['generated_from_trainer'] | false | XLM-R-fine-tuned-for-ner 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: 0.5679 - F1: 0.8378 | 6f2de7ea4c1e59ca87ccf5ed82127661 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.4202 | 1.0 | 2500 | 0.3449 | 0.7963 | | 0.2887 | 2.0 | 5000 | 0.2756 | 0.8057 | | 0.2309 | 3.0 | 7500 | 0.2971 | 0.8040 | | 0.1832 | 4.0 | 10000 | 0.3319 | 0.8167 | | 0.1461 | 5.0 | 12500 | 0.3958 | 0.8350 | | 0.114 | 6.0 | 15000 | 0.4087 | 0.8316 | | 0.0833 | 7.0 | 17500 | 0.4320 | 0.8361 | | 0.0614 | 8.0 | 20000 | 0.4885 | 0.8353 | | 0.039 | 9.0 | 22500 | 0.5408 | 0.8390 | | 0.0251 | 10.0 | 25000 | 0.5679 | 0.8378 | | 8d24d0445495f87be1e3e367c21db17c |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-slowenian-with-lm This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3935 - Wer: 0.3480 | 3dccd287a854ab2b34e60e1432340bde |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 30 - mixed_precision_training: Native AMP | c7fc4974aa2d8cfcb0ddaa4360c253e5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 7.9937 | 2.5 | 100 | 3.1565 | 1.0 | | 3.0466 | 5.0 | 200 | 3.0009 | 0.9992 | | 2.9708 | 7.5 | 300 | 2.9494 | 0.9992 | | 2.0519 | 10.0 | 400 | 0.8874 | 0.7290 | | 0.5773 | 12.5 | 500 | 0.5258 | 0.5037 | | 0.3427 | 15.0 | 600 | 0.4767 | 0.4649 | | 0.2612 | 17.5 | 700 | 0.4549 | 0.4209 | | 0.212 | 20.0 | 800 | 0.4294 | 0.3860 | | 0.1748 | 22.5 | 900 | 0.4085 | 0.3769 | | 0.1587 | 25.0 | 1000 | 0.4017 | 0.3673 | | 0.1435 | 27.5 | 1100 | 0.3927 | 0.3538 | | 0.1314 | 30.0 | 1200 | 0.3935 | 0.3480 | | 8ef967a26d6de3a1f82033d76d2564a8 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-coscan-no-area This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the coscan-speech2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3398 - Accuracy: 0.9486 | 3f1102e2f8aa19434f3dc41035fe8e11 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 | 1d663ea0a47485369171cc83034c5908 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0015 | 1.0 | 6468 | 0.3398 | 0.9486 | | e523d7166ab8ed3daf69acfcee028ac7 |
mit | ['generated_from_trainer'] | false | wonderful_keller This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. | bd00edb13bd7de44397f15847e34459e |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00078, 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'wonderful_keller', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | fe522664f01fceb939ffbfe2ce1153b4 |
apache-2.0 | ['automatic-speech-recognition', 'sv-SE'] | false | exp_w2v2t_sv-se_xlsr-53_s624 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 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | ace4badb37333134a309b63d55b07388 |
apache-2.0 | ['generated_from_trainer'] | false | test-sentiment-model-imdb-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3296 - Accuracy: 0.86 - F1: 0.8618 | 525ad2a40120a04d2e05b2b265059e0a |
apache-2.0 | ['pythae', 'reproducibility'] | false | This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from pythae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_ciwae") ``` | 8f9d14c6c44fce07b2e984eb95f8e62d |
apache-2.0 | ['pythae', 'reproducibility'] | false | Reproducibility This trained model reproduces the results of the official implementation of [1]. | Model | Dataset | Metric | Obtained value | Reference value | |:---:|:---:|:---:|:---:|:---:| | CIWAE (beta=0.05) | Dyn. Binarized MNIST | NLL (5000 IS) | 84.74 (0.01) | 84.57 (0.09) | [1] Rainforth, Tom, et al. "Tighter variational bounds are not necessarily better." International Conference on Machine Learning. PMLR, 2018. | 64a02e2712c3c07a61d794ead6c066c9 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 55b6cc387fd0252d1a06db2042fd101bcea7bb34 pip install -e . cd egs2/slurp_entity/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/slurp_entity_conformer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | e9c9ae6c8566c530e1e4295aa3e68a34 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Thu May 26 14:51:29 EDT 2022` - python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]` - espnet version: `espnet 202204` - pytorch version: `pytorch 1.11.0` - Git hash: `4f36236ed7c8a25c2f869e518614e1ad4a8b50d6` - Commit date: `Thu May 26 00:22:45 2022 -0400` | 615bdb7397306f84379b170a2859b999 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave_10best/devel|8690|178058|82.9|7.8|9.3|2.7|19.8|51.5| |decode_asr_asr_model_valid.acc.ave_10best/test|13078|262176|81.9|7.8|10.3|2.6|20.7|50.7| | e45903e02776fe5a5d5e79745c0e6dc1 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave_10best/devel|8690|847400|89.4|3.1|7.5|3.1|13.7|51.5| |decode_asr_asr_model_valid.acc.ave_10best/test|13078|1245475|88.4|3.1|8.5|3.0|14.6|50.7| | 71944f3f4375de98d2105f084a34eca6 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_e12_d6_size512_lr1e-3_warmup35k.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_e12_d6_size512_lr1e-3_warmup35k_raw_en_word ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 64 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_word/train/speech_shape - exp/asr_stats_raw_en_word/train/text_shape.word valid_shape_file: - exp/asr_stats_raw_en_word/valid/speech_shape - exp/asr_stats_raw_en_word/valid/text_shape.word batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train/wav.scp - speech - kaldi_ark - - dump/raw/train/text - text - text valid_data_path_and_name_and_type: - - dump/raw/devel/wav.scp - speech - kaldi_ark - - dump/raw/devel/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 35000 token_list: - <blank> - <unk> - ▁SEP - ▁FILL - s - ▁the - a - ▁to - ▁i - ▁me - e - ▁s - ▁a - i - ▁you - ▁what - er - ing - u - ▁is - '''' - o - p - ▁in - ▁p - y - ▁my - ▁please - d - c - m - ▁b - l - ▁m - ▁c - st - date - n - ▁d - le - b - ▁for - re - t - ▁on - en - h - 'on' - ar - person - ▁re - ▁f - ▁g - ▁of - an - ▁ - g - ▁today - ▁t - or - ▁it - ▁this - ▁h - r - f - at - ch - ce - place_name - ▁email - ▁do - es - ri - ▁e - ▁w - ic - in - ▁that - event_name - ▁play - ▁and - al - ▁n - ▁can - email_query - ve - ▁new - day - it - ate - ▁from - ▁have - k - time - ▁am - media_type - email_sendemail - ent - ▁olly - qa_factoid - se - v - et - ck - ▁any - calendar_set - ly - th - ▁how - ▁meeting - ed - ▁tell - ▁st - x - ur - ro - ▁at - nd - ▁list - w - ▁u - ou - ▁not - ▁about - ▁an - ▁o - general_negate - ut - ▁time - ▁be - ▁ch - ▁are - social_post - business_name - la - ty - play_music - ot - general_quirky - ▁l - ▁sh - ▁tweet - om - ▁week - um - ▁one - ter - ▁he - ▁up - ▁com - general_praise - weather_query - ▁next - ▁th - ▁check - calendar_query - ▁last - ▁ro - ad - is - ▁with - ay - ▁send - pe - ▁pm - ▁tomorrow - ▁j - un - ▁train - general_explain - ▁v - one - ▁r - ra - news_query - ation - ▁emails - us - if - ct - ▁co - ▁add - ▁will - ▁se - nt - ▁was - ine - ▁de - ▁set - ▁ex - ▁would - ir - ow - ber - general_repeat - ight - ook - ▁again - ▁song - currency_name - ll - ▁ha - ▁go - relation - te - ion - and - ▁y - ▁ye - general_affirm - general_confirm - ery - ▁po - ff - ▁we - ▁turn - ▁did - ▁mar - ▁alarm - ▁like - datetime_query - ers - ▁all - ▁remind - ▁so - qa_definition - ▁calendar - end - ▁said - ci - ▁off - ▁john - ▁day - ss - pla - ume - ▁get - ail - pp - z - ry - am - ▁need - as - ▁thank - ▁wh - ▁want - ▁right - ▁jo - ▁facebook - ▁k - ge - ld - ▁fri - ▁two - general_dontcare - ▁news - ol - oo - ant - ▁five - ▁event - ake - definition_word - transport_type - ▁your - vi - orn - op - ▁weather - ome - ▁app - ▁lo - de - ▁music - weather_descriptor - ak - ke - ▁there - ▁si - ▁lights - ▁now - ▁mo - calendar_remove - our - ▁dollar - food_type - me - ▁more - ▁no - ▁birthday - orrect - ▁rep - ▁show - play_radio - ▁mon - ▁does - ood - ag - li - ▁sto - ▁contact - cket - email_querycontact - ▁ev - ▁could - ange - ▁just - out - ame - . - ▁ja - ▁confirm - qa_currency - ▁man - ▁late - ▁think - ▁some - timeofday - ▁bo - qa_stock - ong - ▁start - ▁work - ▁ten - int - ▁command - all - ▁make - ▁la - j - ▁answ - ▁hour - ▁cle - ah - ▁find - ▁service - ▁fa - qu - general_commandstop - ai - ▁when - ▁te - ▁by - social_query - ard - ▁tw - ul - id - ▁seven - ▁where - ▁much - art - ▁appointment - ver - artist_name - el - device_type - ▁know - ▁three - ▁events - ▁tr - ▁li - ork - red - ect - ▁let - ▁respon - ▁par - zz - ▁give - ▁twenty - ▁ti - ▁curre - play_podcasts - ▁radio - cooking_recipe - transport_query - ▁con - gh - ▁le - lists_query - ▁rem - recommendation_events - house_place - alarm_set - play_audiobook - ist - ase - music_genre - ive - ast - player_setting - ort - lly - news_topic - list_name - ▁playlist - ▁ne - business_type - personal_info - ind - ust - di - ress - recommendation_locations - lists_createoradd - iot_hue_lightoff - lists_remove - ord - ▁light - ere - alarm_query - audio_volume_mute - music_query - ▁audio - rain - ▁date - ▁order - audio_volume_up - ▁ar - ▁podcast - transport_ticket - mail - iot_hue_lightchange - iot_coffee - radio_name - ill - ▁ri - '@' - takeaway_query - song_name - takeaway_order - ▁ra - email_addcontact - play_game - book - transport_traffic - ▁house - music_likeness - her - transport_taxi - iot_hue_lightdim - ment - ght - fo - order_type - color_type - '1' - ven - ould - general_joke - ess - ain - qa_maths - ▁place - ▁twe - cast - iot_cleaning - ▁che - ▁cont - ith - audiobook_name - email_address - game_name - ▁cal - general_frequency - ▁tom - ▁food - act - iot_hue_lightup - '2' - alarm_remove - podcast_descriptor - ▁definition - audio_volume_down - ▁media - email_folder - dia - meal_type - ▁mus - recommendation_movies - ▁ad - ree - pt - now - playlist_name - ▁person - change_amount - ▁pla - escri - datetime_convert - podcast_name - ▁ab - time_zone - ▁def - ting - iot_wemo_on - music_settings - iot_wemo_off - orre - cy - ank - music_descriptor - lar - app_name - row - joke_type - xt - of - ition - ▁meet - ink - ▁confir - transport_agency - general_greet - ▁business - ▁art - ▁ag - urn - escript - rom - ▁rel - ▁au - ▁currency - audio_volume_other - iot_hue_lighton - ▁artist - '?' - ▁bus - cooking_type - movie_name - coffee_type - ingredient - ather - music_dislikeness - sp - q - ▁ser - esc - ▁bir - ▁cur - name - ▁tran - ▁hou - ek - uch - ▁conf - ▁face - '9' - ▁birth - I - sw - transport_descriptor - ▁comm - lease - transport_name - aid - movie_type - ▁device - alarm_type - audiobook_author - '5' - drink_type - ▁joh - ▁defin - word - ▁curren - order - iness - W - cooking_query - sport_type - ▁relation - oint - H - '8' - A - '0' - ▁dol - vice - ▁pers - '&' - T - ▁appoint - _ - '7' - '3' - '-' - game_type - ▁pod - N - M - E - list - music_album - dio - ▁transport - qa_query - C - O - U - query_detail - ']' - '[' - descriptor - ':' - spon - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true joint_net_conf: null use_preprocessor: true token_type: word bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: utterance_mvn normalize_conf: {} model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false extract_feats_in_collect_stats: false preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 8 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 required: - output_dir - token_list version: '202204' distributed: false ``` </details> | 33113b6ab99a84b23ed9fdc530afab30 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Jason-Art Dreambooth model trained by Alexwww with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Putting the prompte words: "photography minimal symmetric" will help get better outputs Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: | e0b46b970643e9dec318434c1d86c88e |
apache-2.0 | ['Android', 'tflite', 'blenderbot'] | false | Model Card `blenderbot-small-tflite` is a tflite version of `blenderbot-small-90M` I converted for my UTA CSE3310 class. See the repo at [https://github.com/kmosoti/DesparadosAEYE](https://github.com/kmosoti/DesparadosAEYE) and the conversion process [here](https://drive.google.com/file/d/1F93nMsDIm1TWhn70FcLtcaKQUynHq9wS/view?usp=sharing). You have to right pad your user and model input integers to make them [32,]-shaped. Then indicate te true length with the 3rd and 4th params. ```python display(interpreter.get_input_details()) display(interpreter.get_output_details()) ``` ```json [{'dtype': numpy.int32, 'index': 0, 'name': 'input_tokens', 'quantization': (0.0, 0), 'quantization_parameters': {'quantized_dimension': 0, 'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32)}, 'shape': array([32], dtype=int32), 'shape_signature': array([32], dtype=int32), 'sparsity_parameters': {}}, {'dtype': numpy.int32, 'index': 1, 'name': 'decoder_input_tokens', 'quantization': (0.0, 0), 'quantization_parameters': {'quantized_dimension': 0, 'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32)}, 'shape': array([32], dtype=int32), 'shape_signature': array([32], dtype=int32), 'sparsity_parameters': {}}, {'dtype': numpy.int32, 'index': 2, 'name': 'input_len', 'quantization': (0.0, 0), 'quantization_parameters': {'quantized_dimension': 0, 'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32)}, 'shape': array([], dtype=int32), 'shape_signature': array([], dtype=int32), 'sparsity_parameters': {}}, {'dtype': numpy.int32, 'index': 3, 'name': 'decoder_input_len', 'quantization': (0.0, 0), 'quantization_parameters': {'quantized_dimension': 0, 'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32)}, 'shape': array([], dtype=int32), 'shape_signature': array([], dtype=int32), 'sparsity_parameters': {}}] [{'dtype': numpy.int32, 'index': 3113, 'name': 'Identity', 'quantization': (0.0, 0), 'quantization_parameters': {'quantized_dimension': 0, 'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32)}, 'shape': array([1], dtype=int32), 'shape_signature': array([1], dtype=int32), 'sparsity_parameters': {}}] ``` | a64717038734a7f6fb2bdecfce463ff5 |
apache-2.0 | [] | false | distilbert-base-en-th-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). | 242534d9be156791dffd7cc28a2304f6 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-th-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-th-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). | ebc81654460918626f0caf8f60ee2bf1 |
apache-2.0 | ['generated_from_trainer'] | false | Article_50v9_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v9_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.7640 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.7782 | f69bc68ce51e5e1223e9d6f0f2c9a453 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 6 | 0.9810 | 0.0918 | 0.0044 | 0.0084 | 0.7772 | | No log | 2.0 | 12 | 0.7952 | 0.0 | 0.0 | 0.0 | 0.7782 | | No log | 3.0 | 18 | 0.7640 | 0.0 | 0.0 | 0.0 | 0.7782 | | cb54dc501b2c55c3bceab17b165d84c4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4733 | 05b631ed88b69822cde4556e4bf8f042 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7122 | 1.0 | 157 | 2.4938 | | 2.5808 | 2.0 | 314 | 2.4249 | | 2.5267 | 3.0 | 471 | 2.4353 | | f04d84e01fadc7b078dfffd674da45be |
mit | ['text-classification', 'generated_from_trainer'] | false | deberta-v3-large-dapt-scientific-papers-pubmed-finetuned-DAGPap22 This model is a fine-tuned version of [domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed](https://huggingface.co/domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 1.0 - F1: 1.0 | 835a3f936a0450b6cc2b00219eeacd22 |
mit | ['text-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 12 - mixed_precision_training: Native AMP | 9f2fa6b9d37ee4b41b629f69866c82f8 |
mit | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2165 | 1.0 | 669 | 0.0218 | 0.9963 | 0.9973 | | 0.0717 | 2.0 | 1338 | 0.0213 | 0.9964 | 0.9974 | | 0.03 | 3.0 | 2007 | 0.0121 | 0.9983 | 0.9988 | | 0.0165 | 4.0 | 2676 | 0.0147 | 0.9976 | 0.9982 | | 0.0072 | 5.0 | 3345 | 0.0000 | 1.0 | 1.0 | | 0.0055 | 6.0 | 4014 | 0.0000 | 1.0 | 1.0 | | 0.0077 | 7.0 | 4683 | 0.0000 | 1.0 | 1.0 | | 0.0 | 8.0 | 5352 | 0.0000 | 1.0 | 1.0 | | 0.0 | 9.0 | 6021 | 0.0000 | 1.0 | 1.0 | | 0.0 | 10.0 | 6690 | 0.0000 | 1.0 | 1.0 | | 0.0 | 11.0 | 7359 | 0.0000 | 1.0 | 1.0 | | 0.0 | 12.0 | 8028 | 0.0000 | 1.0 | 1.0 | | 714accc5aa40d0b0f9e3a7981d1f9baf |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | glpn-nyu-finetuned-diode-221228-072509 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.4012 - Mae: 0.4030 - Rmse: 0.6173 - Abs Rel: 0.3487 - Log Mae: 0.1574 - Log Rmse: 0.2110 - Delta1: 0.4308 - Delta2: 0.6997 - Delta3: 0.8249 | fd5a7047cff4a447b45f459acdcb27f7 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 24 - eval_batch_size: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.15 - num_epochs: 50 - mixed_precision_training: Native AMP | f9c2d51d0240d6976f0657413a156800 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 1.1571 | 1.0 | 72 | 0.6604 | 0.6233 | 0.8403 | 0.5125 | 0.3119 | 0.3691 | 0.1726 | 0.3423 | 0.4877 | | 0.4895 | 2.0 | 144 | 0.4506 | 0.4460 | 0.6404 | 0.4241 | 0.1812 | 0.2299 | 0.3325 | 0.6053 | 0.7943 | | 0.4709 | 3.0 | 216 | 0.4414 | 0.4370 | 0.6305 | 0.4243 | 0.1764 | 0.2253 | 0.3537 | 0.6145 | 0.7988 | | 0.4436 | 4.0 | 288 | 0.4335 | 0.4324 | 0.6285 | 0.4045 | 0.1746 | 0.2245 | 0.3444 | 0.6506 | 0.8096 | | 0.4656 | 5.0 | 360 | 0.4552 | 0.4515 | 0.6328 | 0.4614 | 0.1838 | 0.2307 | 0.3374 | 0.5762 | 0.7722 | | 0.4482 | 6.0 | 432 | 0.4234 | 0.4166 | 0.6233 | 0.3805 | 0.1654 | 0.2179 | 0.4035 | 0.6623 | 0.8130 | | 0.4099 | 7.0 | 504 | 0.4176 | 0.4185 | 0.6238 | 0.3676 | 0.1662 | 0.2150 | 0.3937 | 0.6589 | 0.8153 | | 0.3987 | 8.0 | 576 | 0.4515 | 0.4431 | 0.6300 | 0.4497 | 0.1792 | 0.2283 | 0.3561 | 0.5906 | 0.7781 | | 0.396 | 9.0 | 648 | 0.4235 | 0.4267 | 0.6347 | 0.3591 | 0.1716 | 0.2224 | 0.3934 | 0.6310 | 0.7963 | | 0.3608 | 10.0 | 720 | 0.4312 | 0.4181 | 0.6227 | 0.4022 | 0.1666 | 0.2217 | 0.4014 | 0.6586 | 0.8173 | | 0.3568 | 11.0 | 792 | 0.4322 | 0.4198 | 0.6183 | 0.4047 | 0.1674 | 0.2186 | 0.3870 | 0.6420 | 0.8071 | | 0.3923 | 12.0 | 864 | 0.4225 | 0.4196 | 0.6294 | 0.3630 | 0.1668 | 0.2181 | 0.3910 | 0.6537 | 0.8151 | | 0.3971 | 13.0 | 936 | 0.4086 | 0.4105 | 0.6219 | 0.3541 | 0.1614 | 0.2144 | 0.4234 | 0.6820 | 0.8144 | | 0.372 | 14.0 | 1008 | 0.4127 | 0.4099 | 0.6172 | 0.3668 | 0.1612 | 0.2119 | 0.4046 | 0.6727 | 0.8260 | | 0.3884 | 15.0 | 1080 | 0.4060 | 0.4074 | 0.6176 | 0.3528 | 0.1598 | 0.2119 | 0.4109 | 0.6925 | 0.8225 | | 0.3616 | 16.0 | 1152 | 0.4078 | 0.4092 | 0.6198 | 0.3532 | 0.1615 | 0.2139 | 0.4162 | 0.6791 | 0.8186 | | 0.3504 | 17.0 | 1224 | 0.4202 | 0.4320 | 0.6408 | 0.3613 | 0.1740 | 0.2261 | 0.3769 | 0.6301 | 0.7915 | | 0.3823 | 18.0 | 1296 | 0.4328 | 0.4218 | 0.6182 | 0.4198 | 0.1684 | 0.2207 | 0.3916 | 0.6371 | 0.8113 | | 0.3437 | 19.0 | 1368 | 0.4133 | 0.4138 | 0.6205 | 0.3638 | 0.1636 | 0.2162 | 0.3967 | 0.6761 | 0.8188 | | 0.3739 | 20.0 | 1440 | 0.4040 | 0.4070 | 0.6187 | 0.3486 | 0.1594 | 0.2124 | 0.4214 | 0.6813 | 0.8214 | | 0.3397 | 21.0 | 1512 | 0.4180 | 0.4300 | 0.6360 | 0.3601 | 0.1732 | 0.2239 | 0.3708 | 0.6362 | 0.8006 | | 0.332 | 22.0 | 1584 | 0.4025 | 0.4050 | 0.6182 | 0.3505 | 0.1582 | 0.2114 | 0.4274 | 0.6909 | 0.8275 | | 0.3552 | 23.0 | 1656 | 0.4120 | 0.4179 | 0.6305 | 0.3569 | 0.1650 | 0.2188 | 0.4002 | 0.6753 | 0.8102 | | 0.3804 | 24.0 | 1728 | 0.4093 | 0.4111 | 0.6223 | 0.3594 | 0.1620 | 0.2152 | 0.4068 | 0.6851 | 0.8166 | | 0.3519 | 25.0 | 1800 | 0.4039 | 0.4122 | 0.6237 | 0.3511 | 0.1621 | 0.2137 | 0.4109 | 0.6895 | 0.8171 | | 0.3276 | 26.0 | 1872 | 0.4044 | 0.4117 | 0.6183 | 0.3533 | 0.1623 | 0.2127 | 0.3979 | 0.6824 | 0.8251 | | 0.3167 | 27.0 | 1944 | 0.4091 | 0.4099 | 0.6189 | 0.3600 | 0.1613 | 0.2135 | 0.4069 | 0.6898 | 0.8218 | | 0.3547 | 28.0 | 2016 | 0.4051 | 0.4055 | 0.6192 | 0.3521 | 0.1586 | 0.2119 | 0.4216 | 0.6921 | 0.8256 | | 0.3297 | 29.0 | 2088 | 0.4025 | 0.4091 | 0.6215 | 0.3500 | 0.1605 | 0.2126 | 0.4155 | 0.6960 | 0.8224 | | 0.3305 | 30.0 | 2160 | 0.4040 | 0.4045 | 0.6171 | 0.3507 | 0.1584 | 0.2120 | 0.4281 | 0.6938 | 0.8255 | | 0.34 | 31.0 | 2232 | 0.4036 | 0.4082 | 0.6194 | 0.3492 | 0.1606 | 0.2132 | 0.4196 | 0.6851 | 0.8207 | | 0.3507 | 32.0 | 2304 | 0.4057 | 0.4120 | 0.6245 | 0.3482 | 0.1619 | 0.2148 | 0.4195 | 0.6777 | 0.8172 | | 0.3617 | 33.0 | 2376 | 0.4036 | 0.4098 | 0.6241 | 0.3477 | 0.1606 | 0.2141 | 0.4219 | 0.6871 | 0.8186 | | 0.3268 | 34.0 | 2448 | 0.4015 | 0.4060 | 0.6197 | 0.3440 | 0.1593 | 0.2122 | 0.4326 | 0.6868 | 0.8211 | | 0.3188 | 35.0 | 2520 | 0.4018 | 0.4032 | 0.6154 | 0.3504 | 0.1575 | 0.2107 | 0.4306 | 0.6952 | 0.8250 | | 0.3286 | 36.0 | 2592 | 0.4046 | 0.4103 | 0.6237 | 0.3507 | 0.1611 | 0.2139 | 0.4179 | 0.6883 | 0.8173 | | 0.3279 | 37.0 | 2664 | 0.3995 | 0.3993 | 0.6118 | 0.3460 | 0.1558 | 0.2091 | 0.4401 | 0.6979 | 0.8272 | | 0.3439 | 38.0 | 2736 | 0.4052 | 0.4063 | 0.6196 | 0.3555 | 0.1590 | 0.2117 | 0.4207 | 0.6972 | 0.8256 | | 0.3188 | 39.0 | 2808 | 0.4028 | 0.4028 | 0.6176 | 0.3482 | 0.1574 | 0.2112 | 0.4351 | 0.6916 | 0.8253 | | 0.3334 | 40.0 | 2880 | 0.4059 | 0.4093 | 0.6218 | 0.3534 | 0.1607 | 0.2137 | 0.4201 | 0.6885 | 0.8217 | | 0.3393 | 41.0 | 2952 | 0.4043 | 0.4048 | 0.6193 | 0.3492 | 0.1584 | 0.2118 | 0.4300 | 0.6906 | 0.8246 | | 0.3099 | 42.0 | 3024 | 0.4029 | 0.4041 | 0.6161 | 0.3499 | 0.1583 | 0.2118 | 0.4274 | 0.6966 | 0.8239 | | 0.3339 | 43.0 | 3096 | 0.4032 | 0.4056 | 0.6213 | 0.3515 | 0.1584 | 0.2122 | 0.4257 | 0.6995 | 0.8239 | | 0.3086 | 44.0 | 3168 | 0.4024 | 0.4049 | 0.6173 | 0.3509 | 0.1586 | 0.2120 | 0.4243 | 0.6994 | 0.8227 | | 0.3262 | 45.0 | 3240 | 0.4007 | 0.4035 | 0.6185 | 0.3467 | 0.1575 | 0.2112 | 0.4304 | 0.6994 | 0.8246 | | 0.3265 | 46.0 | 3312 | 0.4017 | 0.4033 | 0.6170 | 0.3495 | 0.1574 | 0.2110 | 0.4271 | 0.7043 | 0.8247 | | 0.3324 | 47.0 | 3384 | 0.4015 | 0.4056 | 0.6192 | 0.3471 | 0.1587 | 0.2119 | 0.4281 | 0.6944 | 0.8220 | | 0.3159 | 48.0 | 3456 | 0.4012 | 0.4036 | 0.6156 | 0.3487 | 0.1581 | 0.2114 | 0.4279 | 0.6982 | 0.8234 | | 0.3238 | 49.0 | 3528 | 0.4017 | 0.4024 | 0.6161 | 0.3499 | 0.1571 | 0.2106 | 0.4304 | 0.7008 | 0.8255 | | 0.3112 | 50.0 | 3600 | 0.4012 | 0.4030 | 0.6173 | 0.3487 | 0.1574 | 0.2110 | 0.4308 | 0.6997 | 0.8249 | | 2b1c768eb4aa6c2758060865b73afca7 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 | 87f8d58e6261dcf8a17942539f0cc9e3 |
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 conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0615 - Precision: 0.9251 - Recall: 0.9363 - F1: 0.9307 - Accuracy: 0.9841 | 679a5380a95dbcacbdd5ec851bc2c5be |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2473 | 1.0 | 878 | 0.0714 | 0.9154 | 0.9178 | 0.9166 | 0.9808 | | 0.0522 | 2.0 | 1756 | 0.0620 | 0.9201 | 0.9348 | 0.9274 | 0.9832 | | 0.031 | 3.0 | 2634 | 0.0615 | 0.9251 | 0.9363 | 0.9307 | 0.9841 | | 0d5d3c8c4a4f1d8520adb6f259336028 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'newspaper', 'ilgiornale', 'repubblica', 'style-transfer'] | false | IT5 Base for News Headline Style Transfer (Repubblica to Il Giornale) 🗞️➡️🗞️ 🇮🇹 This repository contains the checkpoint for the [IT5 Base](https://huggingface.co/gsarti/it5-base) model fine-tuned on news headline style transfer in the Repubblica to Il Giornale direction on the Italian CHANGE-IT dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io). A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach. | 950a815ef10494c110ac8a0499ade61b |
apache-2.0 | ['italian', 'sequence-to-sequence', 'newspaper', 'ilgiornale', 'repubblica', 'style-transfer'] | false | Using the model The model is trained to generate an headline in the style of Il Giornale from the full body of an article written in the style of Repubblica. Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines r2g = pipeline("text2text-generation", model='it5/it5-base-repubblica-to-ilgiornale') r2g("Arriva dal Partito nazionalista basco (Pnv) la conferma che i cinque deputati che siedono in parlamento voteranno la sfiducia al governo guidato da Mariano Rajoy. Pochi voti, ma significativi quelli della formazione politica di Aitor Esteban, che interverrà nel pomeriggio. Pur con dimensioni molto ridotte, il partito basco si è trovato a fare da ago della bilancia in aula. E il sostegno alla mozione presentata dai Socialisti potrebbe significare per il primo ministro non trovare quei 176 voti che gli servono per continuare a governare. \" Perché dovrei dimettermi io che per il momento ho la fiducia della Camera e quella che mi è stato data alle urne \", ha detto oggi Rajoy nel suo intervento in aula, mentre procedeva la discussione sulla mozione di sfiducia. Il voto dei baschi ora cambia le carte in tavola e fa crescere ulteriormente la pressione sul premier perché rassegni le sue dimissioni. La sfiducia al premier, o un'eventuale scelta di dimettersi, porterebbe alle estreme conseguenze lo scandalo per corruzione che ha investito il Partito popolare. Ma per ora sembra pensare a tutt'altro. \"Non ha intenzione di dimettersi - ha detto il segretario generale del Partito popolare , María Dolores de Cospedal - Non gioverebbe all'interesse generale o agli interessi del Pp\".") >>> [{"generated_text": "il nazionalista rajoy: 'voteremo la sfiducia'"}] ``` or loaded using autoclasses: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("it5/it5-base-repubblica-to-ilgiornale") model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-base-repubblica-to-ilgiornale") ``` If you use this model in your research, please cite our work as: ```bibtex @article{sarti-nissim-2022-it5, title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation}, author={Sarti, Gabriele and Nissim, Malvina}, journal={ArXiv preprint 2203.03759}, url={https://arxiv.org/abs/2203.03759}, year={2022}, month={mar} } ``` | 08979503e68035d247b977be671b0836 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-cv-position-classifier 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: 1.6924 - Accuracy: {'accuracy': 0.5780703216130645} - F1: {'f1': 0.5780703216130645} - Precision: {'precision': 0.5780703216130645} | 1c4cbfee8f2205af265c6aaed20cce8c |
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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 256c27cc0a86664cd1717818bd5c8837 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:|:---------------------------------:| | 2.0336 | 1.14 | 1000 | 1.8856 | {'accuracy': 0.5259123479420097} | {'f1': 0.5259123479420097} | {'precision': 0.5259123479420097} | | 1.5348 | 2.28 | 2000 | 1.6924 | {'accuracy': 0.5780703216130645} | {'f1': 0.5780703216130645} | {'precision': 0.5780703216130645} | | 8a0e26e19a323d632b2811af1f41cade |
apache-2.0 | ['translation'] | false | opus-mt-zne-es * source languages: zne * target languages: es * OPUS readme: [zne-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/zne-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/zne-es/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-es/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-es/opus-2020-01-16.eval.txt) | d70c599eb02230addd00961b6df5414f |
apache-2.0 | ['translation'] | false | fra-ara * source group: French * target group: Arabic * OPUS readme: [fra-ara](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-ara/README.md) * model: transformer * source language(s): fra * target language(s): apc ara arq arq_Latn ary arz * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-ara/opus-2020-07-03.zip) * test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-ara/opus-2020-07-03.test.txt) * test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-ara/opus-2020-07-03.eval.txt) | f4301b0810fa7adaba966e2e2f0208e1 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: fra-ara - source_languages: fra - target_languages: ara - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-ara/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['fr', 'ar'] - src_constituents: {'fra'} - tgt_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/fra-ara/opus-2020-07-03.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/fra-ara/opus-2020-07-03.test.txt - src_alpha3: fra - tgt_alpha3: ara - short_pair: fr-ar - chrF2_score: 0.439 - bleu: 14.4 - brevity_penalty: 1.0 - ref_len: 7956.0 - src_name: French - tgt_name: Arabic - train_date: 2020-07-03 - src_alpha2: fr - tgt_alpha2: ar - prefer_old: False - long_pair: fra-ara - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | b5a571f329723f0e3b2dcdac7e7ac2a8 |
apache-2.0 | ['translation'] | false | deu-cat * source group: German * target group: Catalan * OPUS readme: [deu-cat](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-cat/README.md) * model: transformer-align * source language(s): deu * target language(s): cat * model: transformer-align * pre-processing: normalization + SentencePiece (spm12k,spm12k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-cat/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-cat/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-cat/opus-2020-06-16.eval.txt) | fa15d407a2a4e2aee30921f564a2586d |
apache-2.0 | ['translation'] | false | System Info: - hf_name: deu-cat - source_languages: deu - target_languages: cat - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-cat/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['de', 'ca'] - src_constituents: {'deu'} - tgt_constituents: {'cat'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm12k,spm12k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/deu-cat/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/deu-cat/opus-2020-06-16.test.txt - src_alpha3: deu - tgt_alpha3: cat - short_pair: de-ca - chrF2_score: 0.5820000000000001 - bleu: 37.4 - brevity_penalty: 0.956 - ref_len: 5507.0 - src_name: German - tgt_name: Catalan - train_date: 2020-06-16 - src_alpha2: de - tgt_alpha2: ca - prefer_old: False - long_pair: deu-cat - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 377e2ac16d2db5c27a3e40689c4a53f6 |
mit | ['vision'] | false | LiLT-RoBERTa (base-sized model) Language-Independent Layout Transformer - RoBERTa model by stitching a pre-trained RoBERTa (English) and a pre-trained Language-Independent Layout Transformer (LiLT) together. It was introduced in the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Wang et al. and first released in [this repository](https://github.com/jpwang/lilt). Disclaimer: The team releasing LiLT did not write a model card for this model so this model card has been written by the Hugging Face team. | ebdd34cecc84cff0521ed14094bfc67d |
mit | ['vision'] | false | Model description The Language-Independent Layout Transformer (LiLT) allows to combine any pre-trained RoBERTa encoder from the hub (hence, in any language) with a lightweight Layout Transformer to have a LayoutLM-like model for any language. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/lilt_architecture.jpg" alt="drawing" width="600"/> | 262aae29ad8e0f60f46719222c721bae |
mit | ['vision'] | false | Intended uses & limitations The model is meant to be fine-tuned on tasks like document image classification, document parsing and document QA. See the [model hub](https://huggingface.co/models?search=lilt) to look for fine-tuned versions on a task that interests you. | ad06cdd6de45abc96f2a0c0100f6897b |
mit | ['vision'] | false | BibTeX entry and citation info ```bibtex @misc{https://doi.org/10.48550/arxiv.2202.13669, doi = {10.48550/ARXIV.2202.13669}, url = {https://arxiv.org/abs/2202.13669}, author = {Wang, Jiapeng and Jin, Lianwen and Ding, Kai}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` | 145122085d1992f5ecaf298b0c58a2ca |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 9.0 - GMACs: 1.4 - Activations (M): 6.1 - Image size: 224 x 224 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/rwightman/pytorch-image-models - **Dataset:** ImageNet-1k | ba047664a30c1eaddf0ed3c032d55b63 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('convnext_pico.d1_in1k', pretrained=True) model = model.eval() | dcc2a76120dad3b39bbb10345410bedb |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_pico.d1_in1k', pretrained=True, features_only=True, ) model = model.eval() | a5b5f17f13852b907dec11d0b65a9142 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_pico.d1_in1k', pretrained=True, num_classes=0, | 0ab681caba57e69dc147245696e16a37 |
apache-2.0 | ['image-classification', 'timm'] | false | Citation ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} } ``` ```bibtex @article{liu2022convnet, author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, title = {A ConvNet for the 2020s}, journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2022}, } ``` | 38aab5c959fb0e306e4816f38c917885 |
apache-2.0 | ['generated_from_keras_callback'] | false | nandysoham/1-clustered This model is a fine-tuned version of [Rocketknight1/distilbert-base-uncased-finetuned-squad](https://huggingface.co/Rocketknight1/distilbert-base-uncased-finetuned-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7785 - Train End Logits Accuracy: 0.7917 - Train Start Logits Accuracy: 0.7264 - Validation Loss: 0.9514 - Validation End Logits Accuracy: 0.7734 - Validation Start Logits Accuracy: 0.7014 - Epoch: 1 | 49c3530b7c2bb7f34256db5ac913e3af |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 138, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | ec0d1390dc174e219ab36c88d05ab295 |
apache-2.0 | ['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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.1245 | 0.6957 | 0.6322 | 0.9694 | 0.7590 | 0.6906 | 0 | | 0.7785 | 0.7917 | 0.7264 | 0.9514 | 0.7734 | 0.7014 | 1 | | 0ed5a469fc03c3c18ff6a5619cd08225 |
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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP | 5862d396d1fa112f13be3716ecbd11b8 |
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