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 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-nl-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-nl-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github r... | 20ab60e8a06431ff75a28688202c7afb |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-sst2-sst2-membership This model is a fine-tuned version of [ikevin98/bert-base-uncased-finetuned-sst2](https://huggingface.co/ikevin98/bert-base-uncased-finetuned-sst2) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 1.3100 - Accuracy: 1.0 | 024b81af76f294e37b1d5032239463ec |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 50eb3e19848f441e0b25b563cbe248c9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5125 | 1.0 | 3813 | 1.3100 | 1.0 | | 0d8fc8827ff221db8422165aa3e3bc40 |
mit | ['generated_from_trainer'] | false | roberta-finetuned-ner This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1322 - Precision: 0.9772 - Recall: 0.9782 - F1: 0.9777 - Accuracy: 0.9767 | ee3209bf811954c138ce70a1b271d76f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 253 | 0.1694 | 0.9636 | 0.9555 | 0.9595 | 0.9617 | | 0.4479 | 2.0 |... | 4d2428d8642f090b09c48aae1859dfbd |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-sports-scouting This model is a fine-tuned version of [amanm27/bert-base-uncased-sports](https://huggingface.co/amanm27/bert-base-uncased-sports) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5127 | 887b652bff9bf3bf4cbad6b867f80bf7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 378 | 1.7194 | | 2.0165 | 2.0 | 756 | 1.5709 | | 1.6935 | 3.0 | 1134 | 1.5282 | | 7a4925366194514b5a695e383bc8e1f4 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 6b9f51c18f6586e14e95e78cc7ec4729 |
mit | ['generated_from_trainer'] | false | mBART_slang_to_standard_1 This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0006 - Bleu: 88.9448 - Gen Len: 41.403 | 241eb3a5a1350ce04b798393ff62fb74 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 0.7894 | 1.0 | 2266 | 0.1850 | 58.9932 | 61.974 | | 0.0343 | 2.0 | 4532 | 0.0064 | 88.295 | 41.529 | | 0.0051 |... | 289de785489752e063b80066c7d1326a |
mit | ['generated_from_trainer'] | false | cold_reman_gpu_v1 This model is a fine-tuned version of [ibm/ColD-Fusion](https://huggingface.co/ibm/ColD-Fusion) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4520 - F1: 0.6592 - Roc Auc: 0.7559 - Recall: 0.6197 - Precision: 0.704 | 92d5bf49ade10f4549e1d29de931101a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:------:|:---------:| | No log | 1.0 | 452 | 0.4556 | 0.6 | 0.7160 | 0.5282 | 0.6944 | | 0.4832 | 2.0 | 90... | a47817646915df6c1bb32c08f7f2c55b |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-BERTmodel-A3-allcontents 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: 0.2951 - Accuracy: 0.8814 - F1: 0.4138 | 1c3f40f819af5b1980ede0b708e08344 |
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: 1.6561 | e915c9434d0c6c3cabd9311cc074e9f8 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7525 - Matthews Correlation: 0.5553 | df00e5f11ca13d53687e42c1f4b5da81 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.523 | 1.0 | 535 | 0.5024 | 0.4160 | | 0.3437 | 2.0 | 1070 | 0.5450 | 0.4965 | | 0.2... | 09e25fa1b8b733f4ad7cc1165ac3567c |
apache-2.0 | ['PyTorch', 'tensorflow'] | false | Motivation Traditional BERT models struggle with VMware-specific words (Tanzu, vSphere, etc.), technical terms, and compound words. (<a href =https://medium.com/@rickbattle/weaknesses-of-wordpiece-tokenization-eb20e37fec99>Weaknesses of WordPiece Tokenization</a>) We have created our vBERT model to address the aforem... | bb4f07873a8012ced9919f4ff6fc7df9 |
apache-2.0 | ['PyTorch', 'tensorflow'] | false | How to Use Here is how to use this model to get the features of a given text in PyTorch: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('VMware/vbert-2021-large') model = BertModel.from_pretrained("VMware/vbert-2021-large") text = "Replace me by any text you'd like." e... | 6f418a26cb99d18d866689ffd95f127e |
apache-2.0 | ['PyTorch', 'tensorflow'] | false | - Model performance measures We benchmarked vBERT on various VMware-specific NLP downstream tasks (IR, classification, etc). The model scored higher than the 'bert-base-uncased' model on all benchmarks. | 84c5d0a2c6291647e8ab369471e5c6dc |
apache-2.0 | ['PyTorch', 'tensorflow'] | false | Limitations and bias Since the model is further pretrained on the BERT model, it may have the same biases embedded within the original BERT model. The data needs to be preprocessed using our internal vNLP Preprocessor (not available to the public) to maximize its performance. | 0861cb134cde596983f217e487874864 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - 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 - training_steps: 400 - mixed_precision... | 6d114d9b374979b5deadd47271a2b324 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'zh'] | false | wav2vec2-xls-r-300m-zh-CN 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 - ZH-CN dataset. It achieves the following results on the evaluation set: - Loss: 0.8828 - Wer: 2.0604 | f3b4a1e513a1301b8672ce955d56e6cc |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'zh'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 60.2112 | 0.74 | 500 | 64.8189 | 1.0 | | 8.1128 | 1.48 | 1000 | 6.8997 | 1.0 | | 6.0492 | 2.22 | 1500 | 5.9677 | 1.949... | dea43af5f16d6d9b6044dd501aa59109 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'zh'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test` ```bash python eval.py --model_id samitizerxu/wav2vec2-xls-r-300m-zh-CN --dataset mozilla-foundation/common_voice_7_0 --config zh-CN --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash pyt... | 09c039ffe5f252ab5137765b6d3432cb |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Dutch-Alpino | Feature | Description | | --- | --- | | **Name** | `nl_udv25_dutchalpino_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_ed... | 654a575629cad86768b847f0e7cefe2d |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (1712 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `ADJ\|nom\|basis\|met-e\|mv-n`, `ADJ\|nom\|basis\|met-e\|zonder-n\|stan`, `ADJ\|nom\|basis\... | 1881a0c4ca6869c29f3f42ed5cd562a8 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 98.65 | | `TOKEN_P` | 98.49 | | `TOKEN_R` | 98.82 | | `TOKEN_ACC` | 99.87 | | `SENTS_F` | 90.84 | | `SENTS_P` | 92.62 | | `SENTS_R` | 89.14 | | `TAG_ACC` | 95.60 | | `POS_ACC` | 97.67 | | `MORPH_ACC` | 96.79 | | `DEP_UAS` | 94.66 | | `DEP_LAS` | 92.28 | | `LEMMA_A... | d7d12875e2ec4ccd0bd22647ae8b8337 |
apache-2.0 | ['automatic-speech-recognition', 'id'] | false | exp_w2v2t_id_unispeech_s1 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your ... | 79e83a110c5750522519391179d76f54 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-multilingual-cased-finetuned-misogyny-sexism-out-of-sample-test-opt-EN This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0225 - Ac... | d851f7b3588bd0f5172def63221b4347 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:| | 0.3654 | 1.0 | 2395 | 0.3117 | 0.8590 | 0.3599 | 0.2898 | 0.4747 | 0.14... | 69131045a647e62608aa2867068a090e |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_rte_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.3915 - Accuracy: 0.5271 | b0a7eacef69543212f6728c16b994f7b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4089 | 1.0 | 20 | 0.3932 | 0.5271 | | 0.4081 | 2.0 | 40 | 0.3915 | 0.5271 | | 0.4075 | 3.0 | 60 | 0.3918 | 0.... | c4451b8d3f993c772f7a20ae79421ed0 |
mit | ['generated_from_trainer'] | false | sd-ner-v2 This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) on the source_data_nlp dataset. It achieves the following results on the evaluation set: - Loss: 0.1551 - Accuracy Score: 0.9513 - Precisi... | d01e1db3f3b0a7d9f7098c7b8e36e3ba |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 256 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 2.0 | 9d32f22b31c67cb65804cd7b5cd782dd |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:| | 0.1082 | 1.0 | 785 | 0.1550 | 0.9493 | 0.7826 | 0.8402 | 0.8104 | | 0.073... | 37ec54d8f93dcde90d12bacd20288e34 |
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: 0.1686 - F1: 0.8606 | dc71694a829e1131fc61a498cd0943e3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2819 | 1.0 | 1073 | 0.1800 | 0.8231 | | 0.1484 | 2.0 | 2146 | 0.1655 | 0.8488 | | 0.0928 | 3.0 | 3219 | 0.1686 | 0.8606 | ... | 0a792bf8d2fd172b5da2a961de8af871 |
apache-2.0 | ['text2text-generation', 'generated_from_trainer'] | false | QA2D-t5-small This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [QA2D](https://huggingface.co/datasets/domenicrosati/QA2D). It achieves the following results on the evaluation set: - Loss: 0.3236 - Rouge1: 89.8753 - Rouge2: 81.8104 - Rougel: 85.4253 - Rougelsum: 85.4236 - Bleu: 72.1... | c2d1a8f86c09db152e817cab932e5319 |
apache-2.0 | ['text2text-generation', 'generated_from_trainer'] | false | Model description A t5-model model to convert questions, answer pairs into statements. Due to the way it's been trained the input should be all lower case and punctuation removed. Use with `. ` as the seperator between question and answer. > "where in the world is carmen. abruzzo" > Output: "carmen is in abruzzo" T... | 7c2451052b29b55a339617d02b9ae282 |
apache-2.0 | ['text2text-generation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP | d430866f818041d7166b93716f5ce803 |
apache-2.0 | ['text2text-generation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.3177 | 1.0 | 5060 | 0.3144 | 89.6379 | 81.3168 | 85.2036 | 85.1904 ... | 9ba3250ea31288303ea2380cda27c423 |
mit | ['spacy', 'token-classification'] | false | zh_core_web_sm Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler. | Feature | Description | | --- | --- | | **Name** | `zh_core_web_sm` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ru... | 7cdf4005689981e9919632336dcb5959 |
mit | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 95.85 | | `TOKEN_P` | 94.58 | | `TOKEN_R` | 91.36 | | `TOKEN_F` | 92.94 | | `TAG_ACC` | 89.33 | | `SENTS_P` | 77.85 | | `SENTS_R` | 72.62 | | `SENTS_F` | 75.14 | | `DEP_UAS` | 69.60 | | `DEP_LAS` | 64.08 | | `ENTS_P` | 72.03 | | `ENTS_R` | 64.93 | | `ENTS_F` | 6... | dcf60a71f6ad26afd11c3fec431f9ddf |
apache-2.0 | ['image-classification', 'vision'] | false | PoolFormer (S24 model) PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu et al. and first released in [this repository](https://github.com/sail-... | f2ca88a04efd727047556c60e18253df |
apache-2.0 | ['image-classification', 'vision'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import PoolFormerFeatureExtractor, PoolFormerForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/00000003976... | ea9057fd04a214d12c207432ec759001 |
apache-2.0 | ['image-classification', 'vision'] | false | params | URL | |---------------------------------------|-------------------------|----------|------------------------------------------------------------------| | PoolFormer-S12 | 77.2 | 12M | https://hugg... | ddb4b21d3180e0bead3539878869045c |
apache-2.0 | ['generated_from_keras_callback'] | false | hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep50 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0832 - Epoch: 49 | 908b660780cc6ec0d35a2fa8e5ea4303 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Epoch | |:----------:|:-----:| | 4.1266 | 0 | | 3.5212 | 1 | | 3.4780 | 2 | | 3.4533 | 3 | | 3.4376 | 4 | | 3.4325 | 5 | | 3.4276 | 6 | | 3.4119 | 7 | | 3.3654 | 8 | | 3.2948 | 9 | | 3.2422 | 10 | | ... | e127363cb695768c2fe20e4ee45586d0 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab3 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: 0.4832 - Wer: 0.3419 | b6b3f5180472429ba3a1dbdf4b86860e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.292 | 4.0 | 500 | 0.7903 | 0.6305 | | 0.5022 | 8.0 | 1000 | 0.4497 | 0.4332 | | 0.2129 | 12.0 | 1500 | 0.4998 | 0.3940 | |... | 381a46bede0390b6a519d463abd7e658 |
apache-2.0 | ['generated_from_trainer'] | false | byt5-small-cstop_artificial This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on the cstop_artificial dataset. It achieves the following results on the evaluation set: - Loss: 0.0414 - Exact Match: 0.8283 | 5de034dd1fbf62bd283fad6ce2f43417 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - trai... | 154d170a2c0c6f4dd77f77b02ce7ddf4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | |:-------------:|:-----:|:----:|:---------------:|:-----------:| | 0.2091 | 25.0 | 200 | 0.0555 | 0.0107 | | 0.0129 | 50.0 | 400 | 0.0414 | 0.0411 | | 0.004 | 75.0 | 600 | 0.0483 ... | 44cac8d66fa91988547de646f8146d7e |
mit | ['generated_from_trainer'] | false | deberta-base-finetuned-sst2 This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2411 - Accuracy: 0.9495 | b8c8ecd532b31ac166af633489444f64 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.1946 | 1.0 | 4210 | 0.2586 | 0.9278 | | 0.1434 | 2.0 | 8420 | 0.2296 | 0.9472 | | 0.1025 | 3.0 | 12630 | 0.2411 ... | 837266690e37974b68eed852926bc1af |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-vitrinaTest1 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9784 - Rouge2 Precision: 0.1556 - Rouge2 Recall: 0.11 - Rouge2 Fmeasure: 0.1243 | 85d8831ebbe0077ccda0b33b3684fb95 |
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 | 50 | 11.7400 | 0.0162 | 0.0139 | 0.0148 ... | fe1a9ebea8cfcd3fe62cd61904a8f515 |
cc-by-sa-3.0 | ['question-answering', 'extractive-qa'] | false | Description A Japanese Question Answering model fine-tuned on [JaQuAD](https://huggingface.co/datasets/SkelterLabsInc/JaQuAD). Please refer [BERT base Japanese](https://huggingface.co/cl-tohoku/bert-base-japanese) for details about the pre-training model. The codes for the fine-tuning are available at [SkelterLabsInc... | 2e51bfe00e0fbad97cb2abb49f876a3c |
cc-by-sa-3.0 | ['question-answering', 'extractive-qa'] | false | Usage ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer question = 'アレクサンダー・グラハム・ベルは、どこで生まれたの?' context = 'アレクサンダー・グラハム・ベルは、スコットランド生まれの科学者、発明家、工学者である。世界初の>実用的電話の発明で知られている。' model = AutoModelForQuestionAnswering.from_pretrained( 'SkelterLabsInc/bert-base-japanese-jaquad') tokenizer ... | 7c51e4cde9408d7c6b8af0eb98dfb878 |
cc-by-sa-3.0 | ['question-answering', 'extractive-qa'] | false | 1 is added to `answer_end` because the index pointed by score is inclusive. answer_end = torch.argmax(answer_end_scores) + 1 answer = tokenizer.convert_tokens_to_string( tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])) | 1467c20bcf5b4d14ffa6b33a3024f906 |
cc-by-sa-3.0 | ['question-answering', 'extractive-qa'] | false | Citation ```bibtex @misc{so2022jaquad, title={{JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension}}, author={ByungHoon So and Kyuhong Byun and Kyungwon Kang and Seongjin Cho}, year={2022}, eprint={2202.01764}, archivePrefix={arXiv}, primaryClass={cs.CL} }... | 2bfeecd65c0ca77b3db8bff55d3cd2dc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.5571 | 1.0 | 2249 | 6.4684 | | 6.1921 | 2.0 | 4498 | 6.1984 | | 6.0016 | 3.0 | 6747 | 6.1112 | | f69cb67af30f677d689a64d7b749a4a7 |
apache-2.0 | ['generated_from_trainer'] | false | python-bytes-distilgpt2 This model is not affiliated with the Python Bytes podcast in any way. This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on [Python Bytes show notes](https://github.com/mikeckennedy/python_bytes_show_notes/tree/master/transcripts). It achieves the followin... | 2f7fa018ee7699f5593514d18363ea36 |
apache-2.0 | ['generated_from_trainer', 'sibyl'] | false | bert-base-uncased-imdb This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4942 - Accuracy: 0.9126 | 32dd2142e282717e2d8edaba71d156f9 |
apache-2.0 | ['generated_from_trainer', 'sibyl'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - 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_steps: 1546 - training_steps: 15468 | 742178fb1c9652f9ea6fd808ed25a8db |
apache-2.0 | ['generated_from_trainer', 'sibyl'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3952 | 0.65 | 2000 | 0.4012 | 0.86 | | 0.2954 | 1.29 | 4000 | 0.4535 | 0.892 | | 0.2595 | 1.94 | 6000 | 0.4320 ... | 1f1d51956b701fb6ecfa483f76beeada |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | This model is very versatile but without negative prompts it will mostly produce images of foxes or other furry creatures. To get the most out of this model you MUST use negative prompts... example- If you want to make a creature that is not a fox.. Use negative prompt: 'fox' If you want to make landscapes/backgro... | 74b4ea210b52796de2d1a1a65e654b1c |
apache-2.0 | [] | false | MobileNet V2 model from Torchvision fine-tuned for Imagenette dataset. Checkpoint trained for 100 epoches using https://github.com/alexsu52/mobilenet_v2_imagenette. Top-1 accuracy is 98.64%. The main intent is to use it in samples and demos for model optimization. Here is the advantages: - Imagenette can automaticall... | 504f5539ff5052814776ee03d7e52494 |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2t_de_vp-it_s962 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 0074ee9e19794082c77ca98b1ef3529d |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-DT This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6697 - Precision: 0.2381 - Recall: 0.0321 - F1: 0.0565 - Accuracy: 0.8179 | 99722d9ee80f489ca71ca914ac910cd9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 99 | 0.7505 | 0.0 | 0.0 | 0.0 | 0.8196 | | No log | 2.0 |... | 3b2c33adfbd75efa83c68f1b0b14f52f |
apache-2.0 | ['generated_from_trainer'] | false | bert-small-eurlex This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the eurlex dataset. It achieves the following results on the evaluation set: - Loss: 1.4260 | f40758cda02080649ebf3d6a061f1660 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 10 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 80 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_ep... | 3a8c322e9c5a537ae25f5b43e651d407 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.9536 | 1.5 | 1000 | 2.0670 | | 2.0331 | 3.0 | 2000 | 1.7540 | | 1.8046 | 4.5 | 3000 | 1.5993 | | 1.678 | 6.0 | 4000 | 1.5039 ... | 70c43d5538a0e8430e561ee8a47290a6 |
apache-2.0 | ['translation'] | false | opus-mt-ase-sv * source languages: ase * target languages: sv * OPUS readme: [ase-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ase-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | 3009b86d6b6ae7b03229bd9e756bbd16 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-hiddentest This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 3.9223 - Bleu: 0.4773 - Gen Len: 51.3902 | c48969ea446f4f271cf48032c4a69412 |
apache-2.0 | ['translation'] | false | opus-mt-fi-ty * source languages: fi * target languages: ty * OPUS readme: [fi-ty](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-ty/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://... | cb79e6d14088e4301362754cd949eb63 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-irish-local This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 2.0788 - Wer: 0.7527 | 831162da6c5f5399e59a2f34d0405918 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | de28f11152a062b105427bc48c43804d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 8.3839 | 2.94 | 50 | 3.3021 | 1.0 | | 3.0703 | 5.88 | 100 | 3.1749 | 1.0 | | 3.1744 | 8.82 | 150 | 3.0452 | 1.0 | |... | e61fdbf979bc6d364f4a4771d31063d8 |
mit | ['generated_from_trainer'] | false | deberta-v3-base-finetuned-rte This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8234 - Accuracy: 0.8195 | 542493b7c0fbd4beab03bd32d1376f48 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 156 | 0.5610 | 0.7545 | | No log | 2.0 | 312 | 0.6270 | 0.7617 | | No log | 3.0 | 468 | 0.6565 | 0.... | 1df9c6288cfb15aeec699cd391553a27 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'sr'] | false | wav2vec2-large-xls-r-300m-sr-v4 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SR dataset. It achieves the following results on the evaluation set: - Loss: 0.5570 - Wer: 0.3038 | f54158ffe065c24ecf60dafa3e74d9e6 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'sr'] | false | Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sr-v4 --dataset mozilla-foundation/common_voice_8_0 --config sr --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data python ev... | 442354110aeade97ec1ea7b84f84d507 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'sr'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 040a0d1d31a475961c99f7caf2316257 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'sr'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 8.2934 | 7.5 | 300 | 2.9777 | 0.9995 | | 1.5049 | 15.0 | 600 | 0.5036 | 0.4806 | | 0.3263 | 22.5 | 900 | 0.5822 | 0.4055 | |... | 59cb423016d0089e251de3fd5c5b71db |
mit | [] | false | Introduction GPorTuguese-2 (Portuguese GPT-2 small) is a state-of-the-art language model for Portuguese based on the GPT-2 small model. It was trained on Portuguese Wikipedia using **Transfer Learning and Fine-tuning techniques** in just over a day, on one GPU NVIDIA V100 32GB and with a little more than 1GB of tra... | 655e0f6cce43a8dc218127bd2be62153 |
mit | [] | false | params | Model file (pt/tf) | Arch. | Training /Validation data (text) | |-------------------------|---------|--------------------|-------------|------------------------------------------| | `gpt2-small-portuguese` | 124M | 487M / 475M | GPT-2 small | Portuguese Wikipedia (1.28 GB / 0.32 GB) | | 8767965d6e676386165976691c14bbd8 |
mit | [] | false | Evaluation results In a little more than a day (we only used one GPU NVIDIA V100 32GB; through a Distributed Data Parallel (DDP) training mode, we could have divided by three this time to 10 hours, just with 2 GPUs), we got a loss of 3.17, an **accuracy of 37.99%** and a **perplexity of 23.76** (see the validation res... | baa869cf38bc92660d9603cf8ab54628 |
mit | [] | false | gpt-2)* Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in this [paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at this [page](https://openai.com/blog/better-language-... | d1a06383172e2579fc7b55dc32373cef |
mit | [] | false | Load GPorTuguese-2 and its sub-word tokenizer (Byte-level BPE) ```python from transformers import AutoTokenizer, AutoModelWithLMHead import torch tokenizer = AutoTokenizer.from_pretrained("pierreguillou/gpt2-small-portuguese") model = AutoModelWithLMHead.from_pretrained("pierreguillou/gpt2-small-portuguese") | 47d9ff87f6d58e802ea1b697e4fbe201 |
mit | [] | false | Load GPorTuguese-2 and its sub-word tokenizer (Byte-level BPE) ```python from transformers import AutoTokenizer, TFAutoModelWithLMHead import tensorflow as tf tokenizer = AutoTokenizer.from_pretrained("pierreguillou/gpt2-small-portuguese") model = TFAutoModelWithLMHead.from_pretrained("pierreguillou/gpt2-small-portu... | a5ad19bd8193e7514dc97c0036f16963 |
mit | [] | false | model output using Top-k sampling text generation method outputs = model.generate(inputs, eos_token_id=50256, pad_token_id=50256, do_sample=True, max_length=40, top_k=40) print(tokenizer.decode(outputs[0])) | 007734306475997d3215e2bc2f93c820 |
mit | [] | false | Limitations and bias The training data used for this model come from Portuguese Wikipedia. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their model card: > Because large-scale language models like GPT-2 do not distinguish fac... | 42ff400448315d2e15b7f23adc54c575 |
mit | [] | false | Author Portuguese GPT-2 small was trained and evaluated by [Pierre GUILLOU](https://www.linkedin.com/in/pierreguillou/) thanks to the computing power of the GPU (GPU NVIDIA V100 32 Go) of the [AI Lab](https://www.linkedin.com/company/ailab-unb/) (University of Brasilia) to which I am attached as an Associate Research... | 17166927911391bdc46ed59735d5fc83 |
mit | [] | false | Citation If you use our work, please cite: ```bibtex @inproceedings{pierre2020gpt2smallportuguese, title={GPorTuguese-2 (Portuguese GPT-2 small): a Language Model for Portuguese text generation (and more NLP tasks...)}, author={Pierre Guillou}, year={2020} } ``` | 5ffaf83fdd6f9e306eee3cd542f02b09 |
mit | [] | false | Sakimi Style on Stable Diffusion This is the `<sakimi>` 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... | c057ad90351ebf2bf4c45d1a55358e9c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b pip install -e . cd egs2/commonvoice/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/french_commonvoice_blstm ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 2e93779e410d2e7aee47c3bee94faa5f |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Fri Apr 29 17:20:37 EDT 2022` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 0.10.6a1` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `716eb8f92e19708acfd08ba3bd39d40890d3a84b` - Commit date: `Thu Apr 28 19:50:59 2022 -0400` | 8b0a655cc42215d0e8b1847bb181a430 |
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