license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.3975 | 0.8122 | 0.8795 | | No log | 2.0 | 390 | 0.4376 | 0.8085 | 0.8673 | | 0.3169 |... | 2de7511ba31eb5818c01dafdfe70c81f |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | nmp This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the custom dataset. It achieves the following results on the evaluation set: - Loss: 0.0803 - Accuracy: 0.9688 | 8fa347da0e6c613a1f92c946fd06ad24 |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 19 | 1.4407 | 0.3438 | | No log | 2.0 | 38 | 0.6772 | 0.9375 | | No log | 3.0 | 57 | 0.2852 | 0.... | ae8f1f4b3f42ad01f78c752fb8efcfb4 |
apache-2.0 | ['azureml', 't5', 'summarization', 'deepspeed'] | false | `t5-3b-samsum-deepspeed` This model was trained using Microsoft's `AzureML` and `DeepSpeed`'s ZeRO 2 optimization. It was fine-tuned on the `SAMSum` corpus from `t5-3b` checkpoint. More information on the fine-tuning process (includes samples and benchmarks): *(currently still WIP, updates coming soon: 7/6/21~7/9/2... | 31333c8843a4ccfac50acf3f49acadd1 |
apache-2.0 | ['azureml', 't5', 'summarization', 'deepspeed'] | false | Resource Usage These results are retrieved from AzureML Studio's resource monitoring module. All experiments were ran on AzureML's low priority clusters. | key | value | | --- | ----- | | AzureML SKU | ND40rs_v2 (8 X V100 32GB) | | Region | US West 2 | | Run Duration | 43m 51.05s | | Compute Cost (LowPriority/Dedicat... | e60fee97d9f8510fbcbe40f711f98e20 |
apache-2.0 | ['azureml', 't5', 'summarization', 'deepspeed'] | false | Carbon Emissions These results are obtained using `codecarbon`. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime). CodeCarbon: https://github.com/mlco2/codecarbon | key | value | | --- | ----- | | timestamp | 2021-07-06T21:57:39 | | duration | 1841.4621863365173 ... | 5f4640b3f6a2786173def9297457cfd7 |
apache-2.0 | ['azureml', 't5', 'summarization', 'deepspeed'] | false | DeepSpeed Optimizer = `AdamW`, Scheduler = `WarmupDecayLR`, Offload = `none` ```json "zero_optimization": { "stage": 2, "allgather_partitions": true, "allgather_bucket_size": 1000000000, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 1000000000, "contiguous_gradients"... | 90a1d832b3ef38a667294c0dac9f5811 |
apache-2.0 | ['azureml', 't5', 'summarization', 'deepspeed'] | false | Usage ```python from transformers import pipeline summarizer = pipeline("summarization", model="henryu-lin/t5-3b-samsum-deepspeed") conversation = '''Henry: Hey, is Nate coming over to watch the movie tonight? Kevin: Yea, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you t... | 6083881db08125da651f950443373524 |
apache-2.0 | ['azureml', 't5', 'summarization', 'deepspeed'] | false | Results | ROUGE | Score | | ----- | ----- | | eval_rouge1 | 54.7875 | | eval_rouge2 | 30.565 | | eval_rougeL | 45.7625 | | eval_rougeLsum | 50.3915 | | predict_rouge1 | 53.6628 | | predict_rouge2 | 29.0196 | | predict_rougeL | 45.1257 | | predict_rougeLsum | 49.171 | | Metric | Value | | ------ | ----- | | eval_gen_l... | 6edef8166e540d0ab26bd3711b4125fe |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab6 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6532 - Wer: 0.5394 | 1ccff1c713979964ab495b38344a9f92 |
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: 1200 - num_epochs: 40 - mixed_precision_t... | 9aedbde9766856235397ede5a436cd24 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.2874 | 13.89 | 500 | 3.1571 | 1.0 | | 1.3896 | 27.78 | 1000 | 0.6532 | 0.5394 | | 01c7cb4a70812c7d54bbb91fd1c9c944 |
mit | ['generated_from_trainer'] | false | roberta-large-finetuned-clinc This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.1574 - Accuracy: 0.9729 | 80acf09f0dc2c057e406a65b3e55dba0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 239 | 0.8113 | 0.9035 | | No log | 2.0 | 478 | 0.2364 | 0.9548 | | 1.7328 | 3.0 | 717 | 0.1760 | 0.... | e9b4a53988ee73135a7375d0fd0bbcfe |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-auto_and_commute-4-16-5-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: 2.2614 - Accuracy: 0.4289 | ec5f15581e72968cd031c0001c8e7cd3 |
apache-2.0 | ['generated_from_trainer'] | false | t5-text2sql_v1 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0772 - Rouge2 Precision: 0.8835 - Rouge2 Recall: 0.39 - Rouge2 Fmeasure: 0.5088 | a80b0898795a57c3581ad3dd2d4e8892 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | No log | 1.0 | 11 | 1.9420 | 0.0755 | 0.022 | 0.0323 ... | 11e5d8508a0aca677e888cdf8083dac6 |
apache-2.0 | ['generated_from_trainer'] | false | idp-headers 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: 1.6714 - F1: 0.4823 | 55ab44c52af06420c2cf95787adf6cc1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.7995 | 1.0 | 5 | 1.8557 | 0.1629 | | 1.7125 | 2.0 | 10 | 1.7832 | 0.1759 | | 1.6381 | 3.0 | 15 | 1.7243 | 0.4698 | |... | ec282e3bb0d79bfdf28e2fd52b9798b9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_qnli_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3931 - Accuracy: 0.5870 | 799ff6aa5d9a7ac967d2a1eb04927192 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4083 | 1.0 | 410 | 0.3946 | 0.5735 | | 0.3936 | 2.0 | 820 | 0.3931 | 0.5870 | | 0.3843 | 3.0 | 1230 | 0.3935 | 0.... | 63ba78dfc567d8e4399564e55b42befc |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de 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: 0.1375 - F1: 0.8615 | c27bfc9d355134eee1f65a1951a44c06 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 525 | 0.1795 | 0.8092 | | No log | 2.0 | 1050 | 0.1360 | 0.8490 | | No log | 3.0 | 1575 | 0.1375 | 0.8615 | ... | 04bc0a997cc678b192cef1bc19d7d19e |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_xls-r_s156 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input i... | 096ec50e1476cb1499de9137546c8f65 |
creativeml-openrail-m | ['text-to-image'] | false | sjfrdv2 Dreambooth model trained by HusseinHE with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-512 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks... | 217228dc0066288960d55ea0dc726142 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Mr-Men-and-Little-Misses Dreambooth model trained by fffiloni with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Use "Mr what you want" or "Little Miss what you want" prompt to try it ;) For example: mr tiger | littl... | ea8f1341e6056270cbb450268c571a57 |
mit | ['generated_from_trainer'] | false | Kalbert This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on a kannada news dataset. It achieves the following results on the evaluation set: - Loss: 1.5324 | b2efe675d7c4e516756e3f4853796e58 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.5835 | 1.0 | 3953 | 1.7985 | | 1.6098 | 2.0 | 7906 | 1.7434 | | 1.5266 | 3.0 | 11859 | 1.6934 | | 1.5179 | 4.0 | 15812 | 1.6665 ... | 028530af4aa30dc0bc44d4ce76a0e1fc |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2209 - Accuracy: 0.9225 - F1: 0.9226 | 85f506416a39e390b6eecb804effee3d |
mit | [] | false | carasibana on Stable Diffusion This is the `<carasibana>` 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... | 01e8294e0a6e5b281a40141b45e1aa4a |
mit | [] | false | lmoroney-vic on Stable Diffusion This is the `<vic>` 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 trai... | 513be7294fc0c7be3505b4dad71b039d |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-target-glue-cola This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola](https://huggingface.co/muhtasham/tiny-mlm-glue-cola) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7414 - Matthews Correlation: 0.1051 | f210cbaba78bb43ed1b192c702aaf7d5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6094 | 1.87 | 500 | 0.6214 | 0.0 | | 0.6012 | 3.73 | 1000 | 0.6164 | 0.0 | | 0.5... | 88dc4108c3c955db62d9e61bd71bd9c5 |
mit | ['tabular-classification', 'sklearn', 'imodels'] | false | Load the data ```python from datasets import load_dataset import imodels import numpy as np from sklearn.model_selection import GridSearchCV import joblib dataset = load_dataset("imodels/compas-recidivism") df = pd.DataFrame(dataset['train']) X_train = df.drop(columns=['is_recid']) y_train = df['is_recid'].values d... | 93245afbf2df79d792e43f3efbdfa45a |
mit | ['tabular-classification', 'sklearn', 'imodels'] | false | Load the model ```python from huggingface_hub import hf_hub_url, cached_download import joblib import pandas as pd REPO_ID = "imodels/figs-compas-recidivism" FILENAME = "sklearn_model.joblib" model = joblib.load(cached_download( hf_hub_url(REPO_ID, FILENAME) )) | 4d462e27237723892e2bf9394383913d |
apache-2.0 | ['tapas', 'table-question-answering'] | false | TAPAS small 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_small_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was pre-trained on MLM and... | 2b3554a4b76cbb7a98f39758b495239d |
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... | 9926ff5fb5e0e427dc3b67429727e000 |
cc-by-4.0 | ['T5', 'translation', 'summarization', 'question answering', 'reading comprehension'] | false | Usage Example code: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("allegro/plt5-small") model = AutoModel.from_pretrained("allegro/plt5-small") ``` | 47c4a3f97003a9334a324bfa8e2c60e6 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | glpn-nyu-finetuned-diode-221229-103851 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: 0.4367 - Mae: 0.4201 - Rmse: 0.6202 - Abs Rel: 0.4454 - Log Mae: 0.1684 - Log Rmse:... | b76fce4ddc995e2047c1afdf38a9a158 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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: 75 - mixed_precisio... | 967fe609d27a89a49fc74f6800436921 |
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 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 1.0076 | 1.0 | 72 | 0.4910 ... | 1ad6e9f5a8862faa87bea7024e013709 |
mit | ['generated_from_trainer'] | false | fb-data2vec-finetuned-finance-classification This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/facebook/data2vec-text-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8993 - Accuracy: 0.8557 - F1: 0.8563 - Precision: 0.8576 - Recall... | ffc52c58907aaa884e7fc5472f52a0d5 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP | b663230e0f3db4794c4649962364f7df |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 285 | 0.6704 | 0.6680 | 0.6262 | 0.7919 | 0.6680 | | 0.6626 | 2.0 |... | 4f4f74ff59b49f22ab40b23682720449 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change over the course of training, we provi... | 1535301ea12db5a3b6557335e9ac079a |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-410M has not been fine-tuned for down... | 2d975dce953594f9f58657b5abba9542 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-410M to produce factually accurate output. This model was trained ... | 7959ea016a7732db866d97e9cfb56b58 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Training data [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. Common... | 78f68fa7ecd2a2da0529b7ee63f30c23 |
apache-2.0 | ['speech', 'xls_r', 'xls_r_pretrained'] | false | Wav2Vec2-XLS-R-300M [Facebook's Wav2Vec2 XLS-R](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) counting **300 million** parameters.  XLS-R is Facebook AI's large-scale mult... | 3df8b5fa299311308d7d2fce45172d64 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | ro_core_news_sm Romanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler. | Feature | Description | | --- | --- | | **Name** | `ro_core_news_sm` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2v... | 7c033ae61f3d112ad6acdf23338a630a |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (540 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `ARROW`, `Af`, `Afcfp-n`, `Afcfson`, `Afcfsrn`, `Afcmpoy`, `Afcms-n`, `Afp`, `Afp-p-n`, `Afp-poy`, `Afp-srn`, `Afpf--n`, `Afpfp-n`, `Afpfp-ny`, `Afpfpoy`, `Afpfpry`, `Afpfs... | 6d660f0dab5aa2edec2dcd64a1cc4f89 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.80 | | `TOKEN_P` | 99.67 | | `TOKEN_R` | 99.57 | | `TOKEN_F` | 99.59 | | `TAG_ACC` | 95.58 | | `SENTS_P` | 96.01 | | `SENTS_R` | 96.01 | | `SENTS_F` | 96.01 | | `DEP_UAS` | 87.15 | | `DEP_LAS` | 81.29 | | `LEMMA_ACC` | 95.05 | | `POS_ACC` | 92.83 | | `MORPH_A... | 94e7a4dd75f983ee38c4b8048d51bcf8 |
cc-by-sa-4.0 | ['japanese', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pretrained on 青空文庫 texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [deberta-large-japanese-luw-upos](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-luw-upos) and [UD_Japanese-GSDLUW](https://github.com/UniversalDepen... | af8078ad0540decb9c4253b3c48337f7 |
cc-by-sa-4.0 | ['japanese', 'pos', 'dependency-parsing'] | false | text = "+text+"\n" v=[(s,e) for s,e in w["offset_mapping"] if s<e] for i,(s,e) in enumerate(v,1): q=self.model.config.id2label[p[i,h[i]]].split("|") u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n" return... | 5659255b6628493e7fe7ad304de6058f |
mit | [] | false | fAIrytale on Stable Diffusion This is the `<fAIrytale>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also t... | 4988e0bf4aa0fea4a63e30d1af1ade15 |
apache-2.0 | ['text2text-generation'] | false | pip install -q transformers from transformers import AutoModelForSeq2SeqLM, AutoTokenizer checkpoint = "bigscience/mt0-xxl" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt... | aca3fe30179f2ad5bb022694da1f8a73 |
apache-2.0 | ['text2text-generation'] | false | pip install -q transformers accelerate from transformers import AutoModelForSeq2SeqLM, AutoTokenizer checkpoint = "bigscience/mt0-xxl" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto") inputs = tokenizer.encode("Tra... | f9534358c66d1384100650e4651f59a2 |
apache-2.0 | ['text2text-generation'] | false | pip install -q transformers accelerate bitsandbytes from transformers import AutoModelForSeq2SeqLM, AutoTokenizer checkpoint = "bigscience/mt0-xxl" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True) inputs = tokenizer... | 04dc3541a406af1dec171a64c35ed72d |
creativeml-openrail-m | ['text-to-image'] | false | Sample pictures of: sdcid (use that on your prompt)  on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0922 - Accuracy: 0.1127 | 8572ab1f5b3335d7db8b01d77309a3b9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6929 | 25.0 | 500 | 0.7122 | 0.3662 | | 0.6884 | 50.0 | 1000 | 0.7929 | 0.1831 | | 0.6808 | 75.0 | 1500 | 0.9301 | 0.... | 407ad3a77d11c262e1b2bd939934fa87 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 17 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | d8337edd040c195a137cf5f610818d8e |
bsd-3-clause | ['audio-classification'] | false | Audio Spectrogram Transformer (fine-tuned on AudioSet) Audio Spectrogram Transformer (AST) model fine-tuned on AudioSet. It was introduced in the paper [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Gong et al. and first released in [this repository](https://github.com/YuanGongND/ast). D... | b55c0e1c02a3319d5b7f7bf43efe4b48 |
bsd-3-clause | ['audio-classification'] | false | Usage You can use the raw model for classifying audio into one of the AudioSet classes. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/audio-spectrogram-transformer) for more info. | 409401f171e32211100a4f420603b84a |
apache-2.0 | ['Quality Estimation', 'microtransquest'] | false | Using Pre-trained Models ```python from transquest.algo.word_level.microtransquest.run_model import MicroTransQuestModel import torch model = MicroTransQuestModel("xlmroberta", "TransQuest/microtransquest-en_lv-pharmaceutical-smt", labels=["OK", "BAD"], use_cuda=torch.cuda.is_available()) source_tags, target_tags = ... | e6d490cfa90097feeb7973e9d7f7ba4b |
mit | ['coreference-resolution'] | false | LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution [LingMess](https://arxiv.org/abs/2205.12644) is a linguistically motivated categorization of mention-pairs into 6 types of coreference decisions and learn a dedicated trainable scoring function for each category. This significantly impr... | c05efb46d7c7e7a15e7f1bc5bb42aeac |
mit | ['coreference-resolution'] | false | Training on OntoNotes We present the test results on OntoNotes 5.0 dataset. | Model | Avg. F1 | |---------------------------------|---------| | SpanBERT-large + e2e | 79.6 | | Longformer-large + s2e | 80.3 | | **Longformer-large + LingMess** | 81.4 | | c4eb85c8ceee5f0539617ea9538d3b10 |
mit | ['coreference-resolution'] | false | Citation If you find LingMess useful for your work, please cite the following paper: ``` latex @misc{https://doi.org/10.48550/arxiv.2205.12644, doi = {10.48550/ARXIV.2205.12644}, url = {https://arxiv.org/abs/2205.12644}, author = {Otmazgin, Shon and Cattan, Arie and Goldberg, Yoav}, keywords = {Computation a... | ff4bc4a067d00d91f713e099db9b37a9 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'gn', 'robust-speech-event', 'hf-asr-leaderboard'] | false | wav2vec2-xls-r-300m-gn-cv8 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9392 - Wer: 0.7033 | 73f5b37b48d98ef3134634c9d9c3439c |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'gn', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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 - lr_sche... | 52a3d3eb18715e9594f82d639beabd8c |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'gn', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 20.0601 | 5.54 | 100 | 5.1622 | 1.0 | | 3.7052 | 11.11 | 200 | 3.2869 | 1.0 | | 3.3275 | 16.65 | 300 | 3.2162 | 1.0 ... | 7a8bb4605ccac24dff3c6bbfd8aebfc8 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-small_talk-1-16-5 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: 2.3566 - Accuracy: 0.3855 | 5688b440c35fbf5a1ed56b430668e281 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-cola-target-glue-rte This model is a fine-tuned version of [muhtasham/small-mlm-glue-cola](https://huggingface.co/muhtasham/small-mlm-glue-cola) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9023 - Accuracy: 0.6318 | e061a4a6a3c56e22d51bf06f075c0308 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4086 | 6.41 | 500 | 1.2604 | 0.6390 | | 0.0549 | 12.82 | 1000 | 2.3633 | 0.6318 | | 0.0276 | 19.23 | 1500 | 2.9521 | 0.... | 5e0796b32deb90a73a919ef21f1ec3a4 |
gpl-3.0 | ['object-detection', 'computer-vision', 'yolor', 'yolov4'] | false | Model Description [YOLOR:](https://arxiv.org/abs/2105.04206) You Only Learn One Representation: Unified Network for Multiple Tasks. [YOLOR-Pip:](https://github.com/kadirnar/yolor-pip/) Packaged version of the YOLOR repository [Paper Repo:](https://github.com/WongKinYiu/yolor/) Implementation of paper - YOLOR | 5835392759d2f2f2272c4f193b7130ba |
gpl-3.0 | ['object-detection', 'computer-vision', 'yolor', 'yolov4'] | false | Yolov6 Inference ```python from yolor.helpers import Yolor model = Yolor( cfg='yolor/cfg/yolor_p6.cfg', weights='kadirnar/yolor-p6', imgsz=640, device='cuda:0', hf_model=True ) model.classes = None model.conf = 0.25 model.iou_ = 0.45 model.show = False model.save = True model.predict('yolor/data/highway.j... | 554a265f8cb9096a757f8695ed05a93f |
gpl-3.0 | ['object-detection', 'computer-vision', 'yolor', 'yolov4'] | false | BibTeX Entry and Citation Info ``` @article{wang2021you, title={You Only Learn One Representation: Unified Network for Multiple Tasks}, author={Wang, Chien-Yao and Yeh, I-Hau and Liao, Hong-Yuan Mark}, journal={arXiv preprint arXiv:2105.04206}, year={2021} } ``` | bd32cb5978235d24555cd984a2e3ce31 |
mit | ['generated_from_trainer'] | false | kobart_8_5e-5_datav2_min30_lp5.0_temperature1.0 This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8332 - Rouge1: 36.0185 - Rouge2: 12.6783 - Rougel: 23.3148 - Bleu1: 30.2... | fd9944f61b6927b05f6f54fc82be01e4 |
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: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 | 2800672c5a2ed52c9f76e42c6db992f7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:| | 2.5229 | 0.19 | 1000 | 2.993... | f6b04ec634e31d379227ab059fe59117 |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/t5-small-subjqa-movies-qg` This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: movies) via [`lmqg`](https://github.com/asahi417/lm-qu... | 627f48f178135b525b10b0e2c59c431d |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (movies) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asa... | 92bef0d9172501a7f9f1d55e1fa1dc8c |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-small-subjqa-movies-qg") ... | d539a5a656112f8f428ec966de60f178 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-small-subjqa-movies-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.movies.json) | | Score | Type | Dataset | |:-... | 3d9189076beae8e4bef1e02013550830 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: movies - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: lmqg/t5-small-squad - max_length: 512 - max_length_output: 32 - epoch: 4 ... | dcaffbaa8895f258d65c9b3a25ef811b |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-300m-ar-10 This model is a fine-tuned version of [MeshalAlamr/wav2vec2-xls-r-300m-ar-9](https://huggingface.co/MeshalAlamr/wav2vec2-xls-r-300m-ar-9) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 87.0172 - Wer: 0.2017 | 91d3359431a9e29f99208a9c4f4beb38 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | 20a60e4554babf885a88a788eb22f79f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 56.409 | 4.71 | 400 | 81.8407 | 0.2151 | | 84.2726 | 9.41 | 800 | 82.6777 | 0.2237 | | 80.3604 | 14.12 | 1200 | 85.3856 | 0.2226 | |... | 1e2ed06b0a119bf11fc77af03ca9df01 |
mit | ['generated_from_trainer'] | false | predict-perception-bert-blame-concept This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7359 - Rmse: 0.6962 - Rmse Blame::a Un concetto astratto o... | 23a2d953bbc61dbab21dba167e22e90d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Blame::a Un concetto astratto o un'emozione | Mae | Mae Blame::a Un concetto astratto o un'emozione | R2 | R2 Blame::a Un concetto astratto o un'emozione | Cos | Pair | Rank | Neighbors | Rsa | |:-------------:|:-----:|:----... | f59091f73dec187996d0738671d47e8a |
apache-2.0 | ['generated_from_trainer'] | false | sentiment-analysis 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: 0.3124 - Accuracy: 0.8667 - F1: 0.8658 | 5fc0a64e2f28769a70597bc4f40ce567 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-fine-tuned-on-emotion-dataset 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: 0.2138 - Accuracy Score: 0.9275 - F1 Score: 0.9275 | 9b0737f5cdb05a9a9afeb71a1fa8be27 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy Score | F1 Score | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:--------:| | 0.8024 | 1.0 | 250 | 0.3089 | 0.906 | 0.9021 | | 0.2448 | 2.0 | 500 | 0.2138 | 0.9275 ... | b88fbacd23f75ba1e55c78c1ae192141 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad 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: 1.1596 | 2164e18234e44735fa37326e86d3a88b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2265 | 1.0 | 5533 | 1.1572 | | 0.9548 | 2.0 | 11066 | 1.1278 | | 0.7396 | 3.0 | 16599 | 1.1596 | | 16ff8f742976ab8cc5c555ad9f0bc52d |
cc-by-4.0 | ['generated_from_keras_callback'] | false | skandaonsolve/roberta-finetuned-timeentities2 This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0243 - Epoch: 9 | 033bd12bc5ec8fa82edebfa4659773ef |
cc-by-4.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4660, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay':... | 02d064db61d98ca75106b403a6c3d594 |
cc-by-4.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Epoch | |:----------:|:-----:| | 0.5654 | 0 | | 0.3787 | 1 | | 0.2795 | 2 | | 0.2002 | 3 | | 0.1281 | 4 | | 0.0848 | 5 | | 0.0596 | 6 | | 0.0422 | 7 | | 0.0332 | 8 | | 0.0243 | 9 | | 965d4f30d6427d1aa2f4c8a7ea13c18c |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_age_teens-8_sixties-2_s507 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | f3ab779db81a0bcc890d00afba88475d |
mit | ['audio-classification', 'speechbrain', 'embeddings', 'Accent', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'CommonAccent'] | false | DEPRECATED: GO TO: https://huggingface.co/Jzuluaga/accent-id-commonaccent_ecapa GO TO (BEST MODEL): https://huggingface.co/Jzuluaga/accent-id-commonaccent_ecapa <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width=... | ae17abd1cff750b5645d044698bb49d5 |
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