modelId stringlengths 4 81 | tags list | pipeline_tag stringclasses 17
values | config dict | downloads int64 0 59.7M | first_commit timestamp[ns, tz=UTC] | card stringlengths 51 438k | embedding list |
|---|---|---|---|---|---|---|---|
AdapterHub/bert-base-uncased-pf-scicite | [
"bert",
"en",
"dataset:scicite",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
] | text-classification | {
"architectures": null,
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_bea... | 0 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: YSKartal/scibert_scivocab_uncased-finetuned-2-ref_disam
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
... | [
-0.03144378215074539,
-0.011548400856554508,
-0.01938040181994438,
0.025739576667547226,
0.031636834144592285,
0.021224718540906906,
-0.012858363799750805,
-0.0236077681183815,
-0.06177886575460434,
0.056150954216718674,
0.032273802906274796,
-0.02318025566637516,
0.02095445990562439,
0.03... |
Adinda/Adinda | [
"license:artistic-2.0"
] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReach... | [
-0.04936351254582405,
-0.0162846390157938,
-0.008925484493374825,
0.0364079475402832,
0.04077626019716263,
0.0031095598824322224,
-0.021053247153759003,
-0.010267717763781548,
-0.038161639124155045,
0.05706081911921501,
0.024382708594202995,
-0.002896068152040243,
0.03161252290010452,
0.00... |
AethiQs-Max/aethiqs-base_bertje-data_rotterdam-epochs_30-epoch_30 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8 | null | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metr... | [
-0.015056055970489979,
0.013867680914700031,
-0.0026832374278455973,
0.004263303242623806,
0.047999776899814606,
-0.031309593468904495,
0.00672290101647377,
-0.029100432991981506,
-0.012808753177523613,
0.05832337960600853,
0.03354603797197342,
-0.027362609282135963,
0.005346381571143866,
... |
Akash7897/fill_mask_model | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2023-03-27T11:28:50Z | ---
license: openrail
datasets:
- NbAiLab/norwegian-alpaca
library_name: peft
language:
- 'no'
- nb
pipeline_tag: text-generation
---
# NB-Alpaca-LoRA 7B
This is an Norwegian adapter generated by fine-tuning LLaMA-7B on a [Norwegian Alpaca](https://huggingface.co/datasets/NbAiLab/norwegian-alpaca) dataset.
## Usage
... | [
-0.018426012247800827,
-0.019368797540664673,
-0.004972047638148069,
0.0521848127245903,
0.06453343480825424,
0.024162262678146362,
-0.024112805724143982,
-0.021173514425754547,
-0.04525332525372505,
0.06300399452447891,
0.0152176134288311,
-0.01567455567419529,
-0.023944102227687836,
0.03... |
Akash7897/gpt2-wikitext2 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 5 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: BERT-SA
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# BERT-SA
This model is a f... | [
-0.02355588600039482,
-0.008397730998694897,
-0.02010091207921505,
0.035866912454366684,
0.02904164046049118,
0.03170004487037659,
-0.011248468421399593,
-0.00652903039008379,
-0.04264438524842262,
0.04326494038105011,
0.01242035161703825,
-0.021913550794124603,
0.033653195947408676,
0.054... |
Akash7897/my-newtokenizer | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2023-03-27T11:32:32Z | ---
language:
- en
- cs
- multilingual
license: cc-by-sa-4.0
tags:
- bicleaner-ai
tasks:
- text-classification
---
# Bicleaner AI full model for en-cs
Bicleaner AI is a tool that aims at detecting noisy sentence pairs in a parallel corpus. It
indicates the likelihood of a pair of sentences being mutual trans... | [
0.0046531991101801395,
-0.021619131788611412,
-0.0009072475368157029,
0.048166293650865555,
0.019254451617598534,
0.01885511353611946,
-0.002300100401043892,
-0.011960829608142376,
-0.043527841567993164,
0.04335097596049309,
0.016609778627753258,
0.030668510124087334,
0.01889103464782238,
... |
Akashamba/distilbert-base-uncased-finetuned-ner | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2023-03-27T11:38:17Z | ---
language:
- en
- de
- multilingual
license: cc-by-sa-4.0
tags:
- bicleaner-ai
tasks:
- text-classification
---
# Bicleaner AI full model for en-de
Bicleaner AI is a tool that aims at detecting noisy sentence pairs in a parallel corpus. It
indicates the likelihood of a pair of sentences being mutual trans... | [
0.0037542199715971947,
-0.024078505113720894,
-0.00040707262814976275,
0.04282401129603386,
0.02101100981235504,
0.02169105038046837,
-0.003425565781071782,
-0.015304984524846077,
-0.041682176291942596,
0.04426058754324913,
0.012270756997168064,
0.02543405443429947,
0.012853631749749184,
0... |
Akashpb13/Central_kurdish_xlsr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ckb",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 10 | null | ---
language:
- en
- da
- multilingual
license: cc-by-sa-4.0
tags:
- bicleaner-ai
tasks:
- text-classification
---
# Bicleaner AI full model for en-da
Bicleaner AI is a tool that aims at detecting noisy sentence pairs in a parallel corpus. It
indicates the likelihood of a pair of sentences being mutual trans... | [
0.003056467045098543,
-0.023246465250849724,
0.0063472287729382515,
0.04847145453095436,
0.02159355767071247,
0.019249044358730316,
-0.0021137988660484552,
-0.01511081587523222,
-0.04887745529413223,
0.04374564811587334,
0.015178879722952843,
0.02320466935634613,
0.01307126134634018,
0.054... |
AkshaySg/langid | [
"multilingual",
"dataset:VoxLingua107",
"speechbrain",
"audio-classification",
"embeddings",
"Language",
"Identification",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"VoxLingua107",
"license:apache-2.0"
] | audio-classification | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 2 | null | ---
language:
- en
- sv
- multilingual
license: cc-by-sa-4.0
tags:
- bicleaner-ai
tasks:
- text-classification
---
# Bicleaner AI full model for en-sv
Bicleaner AI is a tool that aims at detecting noisy sentence pairs in a parallel corpus. It
indicates the likelihood of a pair of sentences being mutual trans... | [
0.003470822935923934,
-0.021826788783073425,
0.0005284452927298844,
0.047544997185468674,
0.020184945315122604,
0.017143482342362404,
-0.002973234513774514,
-0.010563992895185947,
-0.0419180653989315,
0.043109871447086334,
0.01563352532684803,
0.029630107805132866,
0.021702822297811508,
0.... |
AlbertHSU/ChineseFoodBert | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 15 | null | # Vocabulary Trimmed [lmqg/mbart-large-cc25-esquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-esquad-qa): `vocabtrimmer/mbart-large-cc25-esquad-qa-trimmed-es`
This model is a trimmed version of [lmqg/mbart-large-cc25-esquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-esquad-qa) by [`vocabtrimmer`](https://g... | [
-0.01806684583425522,
-0.019725432619452477,
-0.01600438356399536,
0.04211955517530441,
0.022340526804327965,
-0.0008755551534704864,
-0.010275852866470814,
0.011064857244491577,
-0.04211307317018509,
0.03758162260055542,
0.010640813037753105,
-0.016904158517718315,
0.027894508093595505,
0... |
Aleksandar/distilbert-srb-base-cased-oscar | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 4 | null | ---
license: bigscience-bloom-rail-1.0
---
We finetune bloom-7b1 using LoRA (Low-rank adatation) with CodeAlpaca dataset from https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k. So we name the trained weight as bloom-7b1-lora-codeaplaca20k. | [
-0.04695961996912956,
0.016462819650769234,
0.018948597833514214,
0.012449764646589756,
0.01653459295630455,
-0.00940459594130516,
-0.014483930543065071,
0.0002553810190875083,
-0.027187881991267204,
0.0658675879240036,
0.045486174523830414,
0.009179703891277313,
0.025756755843758583,
0.02... |
Aleksandar1932/distilgpt2-rock | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 11 | null | ---
language:
- es
- ca
- multilingual
license: cc-by-sa-4.0
tags:
- bicleaner-ai
tasks:
- text-classification
---
# Bicleaner AI full model for es-ca
Bicleaner AI is a tool that aims at detecting noisy sentence pairs in a parallel corpus. It
indicates the likelihood of a pair of sentences being mutual trans... | [
0.0049592480063438416,
-0.02600221522152424,
0.007319715805351734,
0.04417497292160988,
0.019947538152337074,
0.02169930189847946,
-0.007506546564400196,
-0.007232161238789558,
-0.04266505315899849,
0.04192598909139633,
0.00706058694049716,
0.023984821513295174,
0.016321200877428055,
0.048... |
Aleksandra/distilbert-base-uncased-finetuned-squad | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# /content/drive/MyDrive/setfit_tatwa_email_classification/model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been train... | [
-0.00986708514392376,
-0.03258592635393143,
-0.016285663470625877,
0.05867060273885727,
0.029251227155327797,
0.027969691902399063,
-0.02240399643778801,
0.00047760980669409037,
-0.037279583513736725,
0.06759992241859436,
0.028802605345845222,
0.014787621796131134,
0.026685677468776703,
0.... |
AlexMaclean/sentence-compression | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 16 | 2023-03-27T13:25:47Z | ---
license: gpl-3.0
datasets:
- BelleGroup/generated_train_0.5M_CN
- JosephusCheung/GuanacoDataset
language:
- zh
tags:
- alpaca
- Chinese-Vicuna
- llama
---
Checkpoint of Chinese-Vicuna model (https://github.com/Facico/Chinese-Vicuna) finetuned on belle0.5M+guanaco(3epoch).
The model is based on llama7B, so it can ... | [
-0.04470623657107353,
0.0067468550987541676,
0.01900557614862919,
0.031670667231082916,
0.06457997113466263,
0.008760145865380764,
0.01603742130100727,
0.0025392097886651754,
-0.011673299595713615,
0.05670591816306114,
0.01662221923470497,
0.0038299327716231346,
0.023835519328713417,
0.014... |
Alexander-Learn/bert-finetuned-squad-accelerate | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### dreambooth_NgAndrew_BilluA Dreambooth model trained by RafiulCV with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept v... | [
-0.040761549025774,
-0.008801192976534367,
-0.0270664244890213,
0.02777872234582901,
0.031217060983181,
0.012075359001755714,
-0.0009693109313957393,
0.0023216139525175095,
-0.01825009100139141,
0.04227956756949425,
0.0426303930580616,
0.009468378499150276,
-0.031200852245092392,
0.0236374... |
Alexandru/creative_copilot | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: apache-2.0
datasets:
- squad
tags:
- generated_from_trainers
--- | [
-0.023989863693714142,
-0.011685200966894627,
-0.011743445880711079,
0.021517246961593628,
0.07810409367084503,
0.00497883465141058,
-0.02484290488064289,
0.04370807856321335,
-0.02320757508277893,
0.037885185331106186,
0.026761190965771675,
-0.01817895658314228,
0.02703327126801014,
0.034... |
AlexeyYazev/my-awesome-model | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_rew... | [
-0.029807506129145622,
0.01903405226767063,
0.0053086350671947,
0.009276988916099072,
0.04453132301568985,
-0.018908454105257988,
-0.022339046001434326,
-0.01421810220927,
-0.029247628524899483,
0.08463587611913681,
0.018464690074324608,
-0.00794339831918478,
0.01781325414776802,
0.0168817... |
Alfia/anekdotes | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4... | [
0.007444051094353199,
-0.03535175696015358,
0.00309038907289505,
0.03441712260246277,
0.0490306131541729,
0.010740707628428936,
-0.02927643433213234,
-0.00947913620620966,
-0.021605640649795532,
0.037526000291109085,
0.00576018588617444,
-0.009438523091375828,
0.00012629291450139135,
0.025... |
AliPotter24/a | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: balanced-augmented-mlroberta-gest-pred-seqeval-partialmatch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread... | [
-0.01967276632785797,
0.02313380502164364,
-0.0006246240227483213,
0.025796398520469666,
0.034942612051963806,
0.015124429948627949,
-0.02830379828810692,
-0.03998829051852226,
-0.03406447172164917,
0.04606131836771965,
0.016475260257720947,
-0.033211369067430496,
0.0013723969459533691,
0.... |
AliReza/distilbert-emotion | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3-initial
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/... | [
-0.022990422323346138,
-0.017419256269931793,
-0.011512156575918198,
0.029644617810845375,
0.0465516559779644,
-0.001542994868941605,
-0.019495639950037003,
0.003987146075814962,
-0.038119759410619736,
0.053145632147789,
0.015443084761500359,
-0.0059369271621108055,
0.013070953078567982,
0... |
Alicanke/Wyau | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Tech... | [
-0.02909264713525772,
-0.0057164449244737625,
-0.017862742766737938,
0.05199498310685158,
0.0369095653295517,
0.026400411501526833,
-0.0026001641526818275,
-0.03542908653616905,
-0.025133352726697922,
0.047172922641038895,
0.02630671113729477,
-0.008286542259156704,
0.017333872616291046,
0... |
Alireza-rw/testbot | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: bloomify1
---
### bloomify1 Dreambooth model trained by ckao1030 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Col... | [
-0.03392104059457779,
-0.019885879009962082,
-0.011793520301580429,
0.027359632775187492,
0.017159683629870415,
0.01653868518769741,
-0.026929549872875214,
-0.022510550916194916,
-0.021736908704042435,
0.046547193080186844,
0.03513239696621895,
0.019677693024277687,
-0.014705642126500607,
... |
Alireza1044/albert-base-v2-cola | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no... | 32 | 2023-03-27T13:52:23Z | ---
language:
- en
- nn
- multilingual
license: cc-by-sa-4.0
tags:
- bicleaner-ai
tasks:
- text-classification
---
# Bicleaner AI full model for en-nn
Bicleaner AI is a tool that aims at detecting noisy sentence pairs in a parallel corpus. It
indicates the likelihood of a pair of sentences being mutual trans... | [
0.0016674214275553823,
-0.022163964807987213,
-0.000005501020041265292,
0.047660402953624725,
0.01982184313237667,
0.018313955515623093,
-0.001983074937015772,
-0.013694983907043934,
-0.04229765385389328,
0.04310566931962967,
0.017027102410793304,
0.029322084039449692,
0.016834361478686333,
... |
Alireza1044/albert-base-v2-mrpc | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no... | 204 | null | ---
license: openrail
datasets:
- bertin-project/alpaca-spanish
library_name: peft
language:
- es
pipeline_tag: text-generation
---
# BERTIN-Alpaca-LoRA 7B
This is a Spanish adapter generated by fine-tuning LLaMA-7B on a [Spanish Alpaca](https://huggingface.co/datasets/bertin-project/alpaca-spanish) dataset.
## Usag... | [
-0.006943381391465664,
-0.013226390816271305,
-0.013375069946050644,
0.05418535694479942,
0.05402372404932976,
0.021472226828336716,
-0.03131730481982231,
-0.028266720473766327,
-0.013874092139303684,
0.07777377963066101,
0.00733192777261138,
-0.029256293550133705,
-0.026199374347925186,
0... |
Alireza1044/albert-base-v2-qnli | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no... | 41 | 2023-03-27T14:00:21Z | ---
license: gpl-3.0
datasets:
- BelleGroup/generated_train_0.5M_CN
- JosephusCheung/GuanacoDataset
language:
- zh
tags:
- alpaca
- Chinese-Vicuna
- llama
---
Checkpoint of Chinese-Vicuna model (https://github.com/Facico/Chinese-Vicuna) finetuned on belle0.5M+guanaco(about 1.5 epoch).
The model is based on llama7B, s... | [
-0.04381124675273895,
0.007752359379082918,
0.020511625334620476,
0.031031744554638863,
0.06409933418035507,
0.007796306628733873,
0.016764545813202858,
0.002800257410854101,
-0.010762556456029415,
0.055793434381484985,
0.016249606385827065,
0.0035538619849830866,
0.024159230291843414,
0.0... |
Alireza1044/albert-base-v2-qqp | [
"pytorch",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no... | 37 | 2023-03-27T14:00:27Z | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base tem... | [
-0.03477105498313904,
-0.01037435233592987,
0.00011377478222129866,
0.015253852121531963,
0.02282647229731083,
0.044932618737220764,
-0.020595461130142212,
-0.026462821289896965,
-0.028890972957015038,
0.04969323053956032,
0.022915950044989586,
-0.006789288017898798,
0.03135347738862038,
0... |
Alireza1044/albert-base-v2-rte | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no... | 30 | 2023-03-27T14:01:19Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReach... | [
-0.049958210438489914,
-0.01602783240377903,
-0.00872994214296341,
0.03612428158521652,
0.040765535086393356,
0.0027967810165137053,
-0.0211429875344038,
-0.010201436467468739,
-0.0384347140789032,
0.05696588754653931,
0.0243989285081625,
-0.0028609877917915583,
0.03169824928045273,
0.0031... |
Alireza1044/albert-base-v2-sst2 | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no... | 52 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type:... | [
-0.0292340237647295,
0.01823023334145546,
0.003660630900412798,
0.009176398627460003,
0.04400138556957245,
-0.01899772323668003,
-0.021622175350785255,
-0.015784479677677155,
-0.029898837208747864,
0.0846095085144043,
0.01731882616877556,
-0.008446605876088142,
0.017369555309414864,
0.0164... |
Alireza1044/bert_classification_lm | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 35 | 2023-03-27T14:02:48Z | ---
license: gpl-3.0
datasets:
- BelleGroup/generated_train_0.5M_CN
- JosephusCheung/GuanacoDataset
language:
- zh
tags:
- alpaca
- Chinese-Vicuna
- llama
---
Checkpoint of Chinese-Vicuna model (https://github.com/Facico/Chinese-Vicuna) finetuned on belle0.5M+guanaco(about 0.75epoch).
The model is based on llama7B, s... | [
-0.043243445456027985,
0.007695777341723442,
0.020097842440009117,
0.03114045038819313,
0.06378021091222763,
0.007732616271823645,
0.01707882061600685,
0.002291217679157853,
-0.011488605290651321,
0.05636532977223396,
0.017446469515562057,
0.0030978668946772814,
0.024454839527606964,
0.014... |
Alireza1044/michael_bert_lm | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 10 | 2023-03-27T14:05:33Z | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4... | [
0.007366793695837259,
-0.034769799560308456,
-0.005763668101280928,
0.032804008573293686,
0.050174739211797714,
0.013398228213191032,
-0.029720140621066093,
-0.008479499258100986,
-0.02655179426074028,
0.04276404529809952,
0.009034289978444576,
-0.0063350629061460495,
0.0038535534404218197,
... |
AllwynJ/HarryBoy | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | 2023-03-27T14:14:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_ep7_lr2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remo... | [
-0.009005182422697544,
-0.011011043563485146,
-0.01235277857631445,
0.04863603785634041,
0.011756597086787224,
0.021829737350344658,
-0.021189143881201744,
-0.02519809827208519,
-0.03912314027547836,
0.06296546757221222,
0.005399924237281084,
-0.05328846722841263,
0.01865077018737793,
0.04... |
Amir99/toxic | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2023-03-27T14:44:44Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validatio... | [
-0.02581958658993244,
-0.002743205288425088,
0.007008950226008892,
0.018137356266379356,
0.02898196317255497,
0.02592555433511734,
-0.023487230762839317,
-0.011270861141383648,
-0.02306772582232952,
0.05074966326355934,
0.020351707935333252,
-0.04308664798736572,
0.009550035931169987,
0.04... |
Anamika/autonlp-Feedback1-479512837 | [
"pytorch",
"xlm-roberta",
"text-classification",
"unk",
"dataset:Anamika/autonlp-data-Feedback1",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 34 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type:... | [
-0.0292340237647295,
0.01823023334145546,
0.003660630900412798,
0.009176398627460003,
0.04400138556957245,
-0.01899772323668003,
-0.021622175350785255,
-0.015784479677677155,
-0.029898837208747864,
0.0846095085144043,
0.01731882616877556,
-0.008446605876088142,
0.017369555309414864,
0.0164... |
Andranik/TestPytorchClassification | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 36 | null | ---
language:
- as
metrics:
- accuracy
license: bsd
--- | [
-0.020768441259860992,
0.005223515443503857,
-0.016367794945836067,
-0.0035899276845157146,
0.05700313672423363,
0.01056183036416769,
0.0082284826785326,
0.03063042461872101,
-0.057728543877601624,
0.039380598813295364,
0.038242943584918976,
0.016935983672738075,
0.03597760200500488,
0.012... |
Andres2015/HiggingFaceTest | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2023-03-27T15:22:39Z | 项目地址:[LLMPruner:大语言模型裁剪工具](https://github.com/yangjianxin1/LLMPruner)
LLMPruner是一个大语言模型裁剪工具,通过对大语言模型的冗余词表进行裁剪,减少模型参数量,降低显存占用,提升训练速度,并且能够保留预训练中学习到的知识。
本项目对Bloom进行词表裁剪,保留中文token和常用的英文token,词表由250880将至46145,缩减为原来的18.39%。裁剪得到的Bloom模型如下表:
| 裁剪模型 | 原模型... | [
-0.03502851724624634,
-0.009507466107606888,
0.017020678147673607,
0.019549628719687462,
0.019897792488336563,
-0.009500615298748016,
-0.008246424607932568,
-0.03200328350067139,
-0.038599561899900436,
0.04375406354665756,
0.043398551642894745,
-0.006872586440294981,
0.02858131378889084,
0... |
Anji/roberta-base-squad2-finetuned-squad | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.76
... | [
-0.017585376277565956,
-0.014371040277183056,
-0.005864160135388374,
0.0235298965126276,
0.04757902771234512,
0.0021611456759274006,
-0.018146082758903503,
0.008559731766581535,
-0.03776989132165909,
0.056633494794368744,
0.019736386835575104,
-0.007509590592235327,
0.014361836016178131,
0... |
AnonymousSub/AR_rule_based_roberta_only_classfn_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 2 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- SebasV/autotrain-data-tableros_factibilidad
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_ti... | [
-0.003977534826844931,
-0.018265414983034134,
0.020521752536296844,
0.044616393744945526,
0.04409988597035408,
0.003090657526627183,
-0.020999861881136894,
-0.004344075918197632,
-0.03247898072004318,
0.06325522810220718,
-0.008397177793085575,
0.00005385887197917327,
0.003487886395305395,
... |
AnonymousSub/AR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 6 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- SebasV/autotrain-data-tableros_factibilidad
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_ti... | [
-0.003475289326161146,
-0.018237151205539703,
0.019601672887802124,
0.042640578001737595,
0.046031031757593155,
0.0041330549865961075,
-0.02212713286280632,
-0.005606519058346748,
-0.03279869258403778,
0.06400923430919647,
-0.007379316724836826,
-0.0005826918059028685,
0.004243892151862383,
... |
AnonymousSub/AR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 6 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- SebasV/autotrain-data-tableros_factibilidad
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_ti... | [
-0.0042118169367313385,
-0.018689574673771858,
0.019324112683534622,
0.043555956333875656,
0.045388419181108475,
0.0037950994446873665,
-0.02150745503604412,
-0.004047855269163847,
-0.03311826288700104,
0.06353114545345306,
-0.008981854654848576,
-0.000889600720256567,
0.00452097924426198,
... |
AnonymousSub/AR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_qa_model
This model... | [
-0.02345759980380535,
-0.012651647441089153,
-0.013048598542809486,
0.042776331305503845,
0.04141538217663765,
0.004768810234963894,
-0.004191673826426268,
-0.0008484050049446523,
-0.05319565534591675,
0.05100876837968826,
0.0049513247795403,
-0.02236834354698658,
0.013933132402598858,
0.0... |
AnonymousSub/SR_consert | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 2 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ViditRaj/Distil_BERT_Hindi_Ads_Classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment.... | [
-0.012143019586801529,
-0.020734045654535294,
-0.024008022621273994,
0.024121608585119247,
0.03286257013678551,
0.033966924995183945,
-0.024685777723789215,
-0.026950106024742126,
-0.029166748747229576,
0.06603345274925232,
0.009662560187280178,
-0.03627678006887436,
0.018849680200219154,
... |
AnonymousSub/SR_declutr | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 6 | null | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Cleighton071/autotrain-data-detection-for-product-location
co2_eq_emissions:
emissions: 2.30199726014708
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 44269111681... | [
-0.019744819030165672,
-0.01982402242720127,
-0.0020663065370172262,
0.03254785016179085,
0.03353197127580643,
0.027104808017611504,
-0.03689976781606674,
-0.012504681944847107,
-0.043638598173856735,
0.08211785554885864,
0.010131299495697021,
0.018471967428922653,
-0.0006137587479315698,
... |
AnonymousSub/SR_rule_based_roberta_bert_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 2 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.939393937587738
---
# rare-puppers
Autogen... | [
-0.011752109974622726,
-0.0023345949593931437,
0.02983015775680542,
0.03362777456641197,
0.040324464440345764,
-0.009049728512763977,
-0.02620835229754448,
-0.021470190957188606,
-0.022733205929398537,
0.05307565629482269,
0.02612140402197838,
0.001170400995761156,
0.004369635600596666,
0.... |
AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 4 | null | # ⚠️ Type of model/library unknown.
# Feel free to open a Pull request
# for integration of the huggingface model hub
# into the corresponding library =) | [
-0.05826929584145546,
-0.019258957356214523,
0.013294370844960213,
0.021497325971722603,
0.00874699279665947,
0.01709185168147087,
-0.03623867407441139,
-0.015010678209364414,
-0.0030674266163259745,
0.04156998172402382,
0.004459739197045565,
0.013746081851422787,
0.040634941309690475,
0.0... |
AnonymousSub/SR_rule_based_twostagequadruplet_hier_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 2 | null | ---
inference: false
language: pt
datasets:
- assin2
---
# BERTimbau base for Recognizing Textual Entailment
This is the [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) model finetuned for
Recognizing Textual Entailment with the [ASSIN 2](https://huggingface.co/d... | [
-0.0387008860707283,
-0.03396071866154671,
0.0008858968503773212,
0.0710003599524498,
0.03382003679871559,
0.023508870974183083,
-0.023154202848672867,
-0.013217552565038204,
-0.025362852960824966,
0.060402609407901764,
-0.013008756563067436,
-0.005924169905483723,
-0.009385650977492332,
0... |
AnonymousSub/SR_rule_based_twostagetriplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 2 | null | ---
inference: false
language: pt
datasets:
- assin2
---
# BERTimbau base for Semantic Textual Similarity
This is the [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) model finetuned for
Semantic Textual Similarity with the [ASSIN 2](https://huggingface.co/datase... | [
-0.03300125151872635,
-0.041402772068977356,
-0.003364566480740905,
0.07569771260023117,
0.03864395245909691,
0.02230341173708439,
-0.017482534050941467,
0.010365338996052742,
-0.025715267285704613,
0.06292339414358139,
-0.0071417842991650105,
-0.011737695895135403,
-0.024102618917822838,
... |
AnonymousSub/SR_rule_based_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 2 | null | ---
inference: false
language: pt
datasets:
- ruanchaves/faquad-nli
---
# BERTimbau base for Question Answering
This is the [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) model finetuned for
Text Simplification with the [FaQUaD-NLI](https://huggingface.co/ruanc... | [
-0.023361120373010635,
-0.042266301810741425,
0.008541242219507694,
0.0758480355143547,
0.026484290137887,
0.003920614719390869,
-0.013029911555349827,
-0.005052396096289158,
-0.01951259933412075,
0.045230280607938766,
-0.012474904768168926,
0.004643517546355724,
-0.0022461512126028538,
0.... |
AnonymousSub/SR_specter | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 5 | null | ---
inference: false
language: pt
datasets:
- ruanchaves/hatebr
---
# BERTimbau base for Offensive Language Detection
This is the [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) model finetuned for
Offensive Language Detection with the [HateBR](https://huggingfa... | [
-0.025388529524207115,
-0.021999340504407883,
0.01120774820446968,
0.06519421190023422,
0.03539644554257393,
0.027366237714886665,
-0.0186686459928751,
-0.011406464502215385,
-0.018151992931962013,
0.0600653812289238,
-0.014305570162832737,
-0.01853817328810692,
-0.01342765986919403,
0.049... |
AnonymousSub/T5_pubmedqa_question_generation | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_s... | 6 | null | ---
inference: false
language: pt
datasets:
- assin
---
# BERTimbau large for Recognizing Textual Entailment
This is the [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) model finetuned for
Recognizing Textual Entailment with the [ASSIN](https://huggingface.co/d... | [
-0.03781486675143242,
-0.034361135214567184,
-0.002802595030516386,
0.0727100595831871,
0.03867538273334503,
0.023107200860977173,
-0.020945334807038307,
-0.014738364145159721,
-0.021531682461500168,
0.062425825744867325,
-0.006957157049328089,
-0.006359897553920746,
-0.01396327093243599,
... |
AnonymousSub/bert_hier_diff_equal_wts_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 1 | null | ---
inference: false
language: pt
datasets:
- ruanchaves/faquad-nli
---
# BERTimbau large for Question Answering
This is the [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) model finetuned for
Text Simplification with the [FaQUaD-NLI](https://huggingface.co/ru... | [
-0.023513291031122208,
-0.04114151746034622,
0.011007358320057392,
0.07580927759408951,
0.029408449307084084,
0.002619786188006401,
-0.013699803501367569,
-0.008259459398686886,
-0.018485775217413902,
0.04805324599146843,
-0.012174637988209724,
0.004704054445028305,
-0.0028883814811706543,
... |
AnonymousSub/bert_mean_diff_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 6 | null | ---
inference: false
language: pt
datasets:
- ruanchaves/hatebr
---
# BERTimbau large for Offensive Language Detection
This is the [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) model finetuned for
Offensive Language Detection with the [HateBR](https://huggin... | [
-0.024367615580558777,
-0.021435247734189034,
0.012528480030596256,
0.06732147932052612,
0.037520647048950195,
0.02488255687057972,
-0.01964789628982544,
-0.014043641276657581,
-0.017647920176386833,
0.06270550191402435,
-0.014809527434408665,
-0.017005441710352898,
-0.013657337054610252,
... |
AnonymousSub/cline-emanuals-s10-AR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 27 | null | ---
inference: false
language: pt
datasets:
- ruanchaves/faquad-nli
---
# mDeBERTa v3 base for Question Answering
This is the [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) model finetuned for
Text Simplification with the [FaQUaD-NLI](https://huggingface.co/ruanchaves/faquad-nli) da... | [
-0.01807767152786255,
-0.051951415836811066,
0.010663635097444057,
0.06686772406101227,
0.025825196877121925,
0.0029740063473582268,
-0.019573401659727097,
-0.000998778035864234,
-0.020350715145468712,
0.04742639511823654,
-0.009227968752384186,
0.009273136034607887,
0.0012002226430922747,
... |
AnonymousSub/consert-s10-SR | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 28 | null | # Vocabulary Trimmed [lmqg/mbart-large-cc25-esquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-esquad-qa): `vocabtrimmer/mbart-large-cc25-esquad-qa-trimmed-es-5000`
This model is a trimmed version of [lmqg/mbart-large-cc25-esquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-esquad-qa) by [`vocabtrimmer`](http... | [
-0.018963497132062912,
-0.019576549530029297,
-0.016408972442150116,
0.040587589144706726,
0.021942399442195892,
-0.0013804844347760081,
-0.01079018134623766,
0.010546108707785606,
-0.041655316948890686,
0.03816373273730278,
0.011603468097746372,
-0.016663167625665665,
0.028485190123319626,
... |
AnonymousSub/consert-techqa | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 4 | null | # Vocabulary Trimmed [lmqg/mbart-large-cc25-frquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qa): `vocabtrimmer/mbart-large-cc25-frquad-qa-trimmed-fr-5000`
This model is a trimmed version of [lmqg/mbart-large-cc25-frquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qa) by [`vocabtrimmer`](http... | [
-0.016061151400208473,
-0.023192692548036575,
-0.020091673359274864,
0.04020841047167778,
0.0167573019862175,
-0.003828942310065031,
-0.010674308985471725,
0.003810472786426544,
-0.038947075605392456,
0.04038028046488762,
0.009315647184848785,
-0.016352901235222816,
0.02074243687093258,
0.... |
AnonymousSub/declutr-emanuals-s10-AR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 29 | null | Access to model c-nemo/bert-for-movie-review-classification is restricted and you are not in the authorized list. Visit https://huggingface.co/c-nemo/bert-for-movie-review-classification to ask for access. | [
-0.023565063253045082,
0.009752117097377777,
-0.004261634778231382,
0.025892706587910652,
0.06045699864625931,
0.01965779811143875,
-0.04202605411410332,
-0.007489842362701893,
-0.03902213275432587,
0.04058760032057762,
0.055014338344335556,
-0.001777874887920916,
0.017715949565172195,
0.0... |
AnonymousSub/declutr-emanuals-s10-SR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 28 | null | # LLama 7B Hugging Face model
This repo hosts model weights and it's for research purpose. If it against some policies that I don't know, feel free to reach out to me and I will delete it.
---
license: other
---
LLaMA-7B converted to work with Transformers/HuggingFace. This is under a special license, please see the... | [
-0.07839387655258179,
-0.0022810189984738827,
0.014907351695001125,
0.02048005536198616,
0.014893246814608574,
0.025358939543366432,
-0.011818422935903072,
-0.016050919890403748,
-0.006651860196143389,
0.06200714781880379,
0.023346545174717903,
-0.025050736963748932,
0.04155949503183365,
0... |
AnonymousSub/declutr-emanuals-techqa | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 4 | 2023-03-27T18:37:58Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
... | [
-0.03755248337984085,
-0.0028269279282540083,
-0.005128616467118263,
0.025691552087664604,
0.04571577161550522,
-0.02140134572982788,
-0.005991218611598015,
-0.027564579620957375,
-0.032788220793008804,
0.06674347072839737,
0.03147536888718605,
-0.023733878508210182,
0.02332543022930622,
0... |
AnonymousSub/declutr-model-emanuals | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 4 | null | ---
language:
- de
pipeline_tag: fill-mask
tags:
- parliamentary protocols
- political texts
widget:
- text: >-
Diese Themen gehören nicht ins [MASK].
---
⚠️ This version is only trained on around 5 million sentences (perplexity w/ adaption: 3.38 and w/o 13.38). The final version trained on around 30 million se... | [
0.012776713818311691,
-0.04085690528154373,
-0.03434523940086365,
0.07292936742305756,
0.04273102432489395,
0.033733971416950226,
-0.016069842502474785,
-0.00753870140761137,
-0.0388956181704998,
0.07073459029197693,
0.029106447473168373,
-0.027412042021751404,
0.0064655449241399765,
0.023... |
AnonymousSub/declutr-model | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 4 | null | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
... | [
-0.054952047765254974,
0.0017529679462313652,
-0.005613784771412611,
0.04999065771698952,
0.027710596099495888,
0.030877942219376564,
-0.00925261341035366,
-0.02075490541756153,
-0.003662274219095707,
0.05143450200557709,
0.027236420661211014,
-0.010828817263245583,
0.003093378385528922,
0... |
AnonymousSub/declutr-model_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 26 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
... | [
-0.03738781809806824,
-0.0025521910283714533,
-0.004844979383051395,
0.025362564250826836,
0.04534696415066719,
-0.021463604643940926,
-0.005703967530280352,
-0.02707013487815857,
-0.03327438235282898,
0.06671096384525299,
0.03230408951640129,
-0.023243669420480728,
0.02277451381087303,
0.... |
AnonymousSub/rule_based_bert_mean_diff_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 4 | null | # Vocabulary Trimmed [lmqg/mbart-large-cc25-frquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qa): `vocabtrimmer/mbart-large-cc25-frquad-qa-trimmed-fr-15000`
This model is a trimmed version of [lmqg/mbart-large-cc25-frquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qa) by [`vocabtrimmer`](htt... | [
-0.016860736533999443,
-0.02270323410630226,
-0.01997322402894497,
0.04063085839152336,
0.016613470390439034,
-0.0035893383901566267,
-0.010167746804654598,
0.004465492442250252,
-0.03884512186050415,
0.041057806462049484,
0.008922580629587173,
-0.01623137667775154,
0.021713467314839363,
0... |
AnonymousSub/rule_based_bert_quadruplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 3 | null | # Vocabulary Trimmed [lmqg/mbart-large-cc25-esquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-esquad-qa): `vocabtrimmer/mbart-large-cc25-esquad-qa-trimmed-es-10000`
This model is a trimmed version of [lmqg/mbart-large-cc25-esquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-esquad-qa) by [`vocabtrimmer`](htt... | [
-0.01885201968252659,
-0.01947924494743347,
-0.016298610717058182,
0.041430726647377014,
0.02169070951640606,
-0.0006629830459132791,
-0.010071524418890476,
0.010902483016252518,
-0.04218735918402672,
0.03869015350937843,
0.010897086001932621,
-0.016526008024811745,
0.028615599498152733,
0... |
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 8 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
... | [
-0.04228264093399048,
-0.016600823029875755,
-0.017668593674898148,
0.03638124838471413,
0.049454864114522934,
-0.006654277443885803,
-0.012909410521388054,
-0.026887420564889908,
-0.028679516166448593,
0.05083151534199715,
0.02155827172100544,
-0.032437074929475784,
0.022556966170668602,
... |
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 4 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/8047/project-sekai-mizuki-akiyama-loha | [
-0.03207904472947121,
-0.029400048777461052,
0.006857155356556177,
-0.01128336414694786,
0.04230525344610214,
0.0006626039976254106,
-0.002383627463132143,
0.002573349280282855,
-0.035925108939409256,
0.056594058871269226,
0.04154389351606369,
0.01744234189391136,
0.0366167351603508,
0.024... |
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 3 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/24787/chitanda-eru-hyouka | [
-0.027712494134902954,
-0.01153593696653843,
0.01456223614513874,
0.022618163377046585,
0.04484599083662033,
-0.006085788831114769,
0.0019924624357372522,
0.0008992471266537905,
-0.045789021998643875,
0.06452076882123947,
0.035986483097076416,
0.01246559526771307,
0.056789591908454895,
0.0... |
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 31 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/17798/elysia-hoh-without-bells | [
-0.06020750105381012,
-0.013621839694678783,
-0.019745366647839546,
-0.007185528054833412,
0.02230186201632023,
0.017163114622235298,
-0.032646168023347855,
0.004657117184251547,
-0.05489882081747055,
0.02468452975153923,
0.03068714030086994,
-0.004507501143962145,
0.036215439438819885,
0.... |
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_wikiqa_copy | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 1 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/24488/morisaki-alesia-yu-blue-reflection-sun | [
-0.034745823591947556,
-0.023346876725554466,
0.006053428631275892,
0.01755312643945217,
0.036545053124427795,
-0.005032933317124844,
0.012677864171564579,
-0.01688925176858902,
-0.05058622360229492,
0.043142467737197876,
0.04151902720332146,
0.011630017310380936,
0.04977276176214218,
0.03... |
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 4 | 2023-03-27T19:37:25Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
... | [
-0.040293335914611816,
-0.016022076830267906,
-0.015937957912683487,
0.035652440041303635,
0.04955359175801277,
-0.005187537055462599,
-0.014415849931538105,
-0.024400679394602776,
-0.032092101871967316,
0.054688192903995514,
0.022667327895760536,
-0.03185446932911873,
0.02064742147922516,
... |
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 3 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/24748/sn-kursk-or-azur-lane-or-lora | [
-0.0427611768245697,
-0.030590320006012917,
-0.03658263385295868,
0.04740006476640701,
0.0486975759267807,
-0.017919791862368584,
-0.0015401485143229365,
0.020370595157146454,
-0.06849657744169235,
0.0454128123819828,
0.0528271459043026,
0.007224590517580509,
-0.016544677317142487,
0.01385... |
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 30 | 2023-03-27T19:40:00Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
... | [
-0.045480500906705856,
-0.0013820299645885825,
-0.021998291835188866,
0.03246447071433067,
0.044006019830703735,
0.01799052208662033,
-0.018560878932476044,
-0.03086501732468605,
-0.03683869540691376,
0.06904061138629913,
0.021958736702799797,
0.0029886646661907434,
0.015047254972159863,
0... |
AnonymousSub/rule_based_hier_triplet_0.1_epochs_1_shard_1_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 2 | 2023-03-27T19:43:35Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: LKD_Experience_CV5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, the... | [
-0.012693223543465137,
0.01144749391824007,
-0.00913962721824646,
0.02310248650610447,
0.036074649542570114,
0.0010275987442582846,
-0.025873437523841858,
-0.011532342061400414,
-0.03926335275173187,
0.0575997456908226,
0.03465401381254196,
-0.04488060250878334,
0.006093134637922049,
0.028... |
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 8 | 2023-03-27T19:47:01Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.74
... | [
-0.019335445016622543,
-0.017632387578487396,
-0.004569720476865768,
0.02498926781117916,
0.04643942415714264,
0.0008719853358343244,
-0.017116151750087738,
0.007295413874089718,
-0.03664317727088928,
0.05801653489470482,
0.018976759165525436,
-0.00710059329867363,
0.009650344029068947,
0.... |
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1_wikiqa_copy | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 1 | null | ---
tags:
- autotrain
- summarization
language:
- de
widget:
- text: "I love AutoTrain 🤗"
datasets:
- fathyshalab/autotrain-data-dialogsumgerman
co2_eq_emissions:
emissions: 86.21246024573398
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 44305111787
- CO2 Emissions (in grams): 86.21... | [
-0.021787656471133232,
-0.01717001013457775,
0.009007870219647884,
0.028574371710419655,
0.032833490520715714,
0.018247248604893684,
-0.03512151911854744,
-0.025658661499619484,
-0.05070709437131882,
0.08150923997163773,
0.018619095906615257,
0.019646070897579193,
0.008440209552645683,
0.0... |
AnonymousSub/rule_based_roberta_hier_quadruplet_0.1_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 6 | null | # Vocabulary Trimmed [lmqg/mbart-large-cc25-frquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qa): `vocabtrimmer/mbart-large-cc25-frquad-qa-trimmed-fr-60000`
This model is a trimmed version of [lmqg/mbart-large-cc25-frquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qa) by [`vocabtrimmer`](htt... | [
-0.01691928319633007,
-0.021475860849022865,
-0.0196334570646286,
0.040800757706165314,
0.017069414258003235,
-0.0033815428614616394,
-0.01016897615045309,
0.0044423844665288925,
-0.038605086505413055,
0.04105594754219055,
0.009373284876346588,
-0.016193117946386337,
0.022405149415135384,
... |
AnonymousSub/rule_based_roberta_hier_quadruplet_0.1_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 4 | 2023-03-27T20:16:08Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
# controlnet- yiyixu/fill-circle-controlnet
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with ... | [
-0.0217069610953331,
0.005088931880891323,
-0.029713589698076248,
0.026203909888863564,
0.03626912832260132,
0.0044169495813548565,
0.01155924517661333,
-0.0016524307429790497,
-0.000876744685228914,
0.04791073873639107,
-0.005859257187694311,
-0.01339685544371605,
0.005090359132736921,
0.... |
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 2 | null | # Vocabulary Trimmed [lmqg/mbart-large-cc25-esquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-esquad-qa): `vocabtrimmer/mbart-large-cc25-esquad-qa-trimmed-es-15000`
This model is a trimmed version of [lmqg/mbart-large-cc25-esquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-esquad-qa) by [`vocabtrimmer`](htt... | [
-0.018598783761262894,
-0.020067453384399414,
-0.01658513769507408,
0.04156964272260666,
0.02212941274046898,
-0.0009654472232796252,
-0.010341586545109749,
0.010558076202869415,
-0.041548632085323334,
0.03865059092640877,
0.011126636527478695,
-0.016438806429505348,
0.02780204266309738,
0... |
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 24 | 2023-03-27T20:31:44Z | # Vocabulary Trimmed [lmqg/mbart-large-cc25-jaquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-jaquad-qa): `vocabtrimmer/mbart-large-cc25-jaquad-qa-trimmed-ja-5000`
This model is a trimmed version of [lmqg/mbart-large-cc25-jaquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-jaquad-qa) by [`vocabtrimmer`](http... | [
-0.01807514950633049,
-0.021207375451922417,
-0.02531679905951023,
0.040934063494205475,
0.022539254277944565,
-0.005604141857475042,
-0.003089777659624815,
0.003937473986297846,
-0.03642088174819946,
0.03804948925971985,
0.0002399819204583764,
-0.03219398483633995,
0.020037390291690826,
0... |
AnonymousSub/rule_based_roberta_hier_triplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 25 | 2023-03-27T20:45:13Z | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
widget:
- src: https://huggingface.co/platzi/platzi-vit-base-beans/resolve/main/healthy.jpeg
example_title: Healthy
- src: https://huggingface.co/platzi/platzi-vit-base-beans/resolve/main/bean_rust.jpeg... | [
0.007824406027793884,
0.0013301392318680882,
0.013655447401106358,
0.01215409580618143,
0.034693192690610886,
-0.014692754484713078,
-0.012782109901309013,
-0.01220631506294012,
-0.01462080329656601,
0.047822531312704086,
-0.004680173937231302,
-0.01163127925246954,
0.008099360391497612,
0... |
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 24 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: chatgpt-gpt4-prompts-bart-large-cnn-samsum
results: []
datasets:
- fka/awesome-chatgpt-prompts
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and comp... | [
-0.03006625548005104,
-0.016948698088526726,
0.009211987257003784,
0.03075745888054371,
0.029937854036688805,
0.004239617846906185,
-0.008048852905631065,
-0.017364276573061943,
-0.024499814957380295,
0.06645061820745468,
0.0308940839022398,
-0.009293374605476856,
0.015974337235093117,
0.0... |
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 25 | 2023-03-27T21:19:04Z | ---
datasets:
- tencups/gpt2
- pietrolesci/gpt3_nli
language:
- en
--- | [
-0.025412186980247498,
-0.014790168963372707,
0.020254144445061684,
0.031303275376558304,
0.06583257764577866,
0.006035967264324427,
0.01473486702889204,
0.004936330020427704,
-0.04033245891332626,
0.036722417920827866,
0.018367653712630272,
-0.010306120850145817,
-0.006813934072852135,
0.... |
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 2 | null | # Vocabulary Trimmed [lmqg/mbart-large-cc25-esquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-esquad-qa): `vocabtrimmer/mbart-large-cc25-esquad-qa-trimmed-es-30000`
This model is a trimmed version of [lmqg/mbart-large-cc25-esquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-esquad-qa) by [`vocabtrimmer`](htt... | [
-0.018638422712683678,
-0.019689258188009262,
-0.016432369127869606,
0.04161236807703972,
0.022496353834867477,
-0.0006515368004329503,
-0.009985871613025665,
0.010211065411567688,
-0.04159240797162056,
0.038472775369882584,
0.01141447015106678,
-0.01662488467991352,
0.02854413539171219,
0... |
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 24 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: CIS6930_DAAGR_Classification
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# CIS6930_DAAGR_Classificat... | [
-0.04069963097572327,
-0.018111854791641235,
-0.007384842727333307,
0.023256082087755203,
0.05385858938097954,
0.0152388671413064,
-0.01705881953239441,
-0.00982894841581583,
-0.021654045209288597,
0.04732890799641609,
0.02985345385968685,
-0.01556830108165741,
-0.0015259833307936788,
0.03... |
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: flan-t5-base-samsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split... | [
-0.01984657719731331,
-0.01185187790542841,
0.001022059004753828,
0.043252572417259216,
0.03694508969783783,
0.007186061702668667,
-0.005653174594044685,
-0.02547037973999977,
-0.042072709649801254,
0.04691625013947487,
0.03334217518568039,
-0.020677080377936363,
0.0017205135663971305,
0.0... |
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 4 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
... | [
-0.040969472378492355,
-0.0165390782058239,
-0.015517286956310272,
0.03638504445552826,
0.04841065779328346,
-0.00490608299151063,
-0.014455602504312992,
-0.025262106209993362,
-0.030841587111353874,
0.05419589579105377,
0.022825857624411583,
-0.03208426758646965,
0.018909623846411705,
0.0... |
AnonymousSub/rule_based_twostage_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 6 | null | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (... | [
-0.040620505809783936,
-0.0013528066920116544,
-0.006780918687582016,
0.04733273386955261,
0.024459004402160645,
0.020490633323788643,
-0.024860762059688568,
-0.032963916659355164,
-0.0036923957522958517,
0.048864420503377914,
0.02012454904615879,
-0.013956459239125252,
0.01799865812063217,
... |
AnonymousSub/rule_based_twostagequadruplet_hier_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 28 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: que_funcione_que_funcione2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# que_func... | [
-0.027890533208847046,
-0.017506860196590424,
0.00007473958976333961,
0.03868144378066063,
0.020353101193904877,
0.004264177288860083,
-0.0032546301372349262,
-0.0032925885170698166,
-0.03442467004060745,
0.0453522726893425,
0.025441110134124756,
-0.009445099160075188,
-0.0037598961498588324... |
AnonymousSub/rule_based_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 5 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unit... | [
-0.02244047075510025,
-0.0064519234001636505,
0.009358084760606289,
0.03722602128982544,
0.032568078488111496,
0.013684649020433426,
-0.027413588017225266,
-0.015120860189199448,
-0.01595662720501423,
0.062332022935152054,
0.005017852410674095,
0.00014994652883615345,
0.012271999381482601,
... |
AnonymousSub/rule_based_twostagetriplet_hier_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 27 | null | # Vocabulary Trimmed [lmqg/mbart-large-cc25-jaquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-jaquad-qa): `vocabtrimmer/mbart-large-cc25-jaquad-qa-trimmed-ja-30000`
This model is a trimmed version of [lmqg/mbart-large-cc25-jaquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-jaquad-qa) by [`vocabtrimmer`](htt... | [
-0.017698083072900772,
-0.020880475640296936,
-0.025999927893280983,
0.04226897284388542,
0.02332957834005356,
-0.005365076009184122,
-0.002319504041224718,
0.003714771941304207,
-0.0359431654214859,
0.03830719739198685,
0.0010564143303781748,
-0.03218046575784683,
0.021055638790130615,
0.... |
AnonymousSub/specter-bert-model | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: que_funcione_que_funcione3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# que_func... | [
-0.029009277001023293,
-0.01991596631705761,
-0.0045479340478777885,
0.03743048012256622,
0.022045589983463287,
0.006717406678944826,
-0.0038273362442851067,
-0.0037846737541258335,
-0.034492719918489456,
0.04691673815250397,
0.027756819501519203,
-0.01114075817167759,
-0.004191129468381405,... |
AnonymousSub/specter-bert-model_copy_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 26 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unit... | [
-0.021556716412305832,
-0.005552652757614851,
0.010528952814638615,
0.0397023968398571,
0.03215514495968819,
0.014833325520157814,
-0.029153723269701004,
-0.015743985772132874,
-0.015217151492834091,
0.061092816293239594,
0.006797544192522764,
0.0007330725202336907,
0.010326417163014412,
0... |
AnonymousSub/specter-bert-model_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 1 | null | ---
language: mt
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- maltese
- whisper-large
- whisper-large-v1
- masri-project
- malta
- university-of-malta
license: cc-by-nc-sa-4.0
widget: null
model-index:
- name: whisper-large-maltese-8k-steps-64h
results:
- task:
name: Automatic Speec... | [
-0.028199834749102592,
-0.013008569367229939,
-0.009161757305264473,
0.05709772929549217,
0.06382536888122559,
0.017697880044579506,
0.005050228908658028,
-0.007516046520322561,
-0.005679038353264332,
0.08000420778989792,
0.03262287378311157,
-0.03901926800608635,
0.006966743152588606,
0.0... |
AnonymousSub/unsup-consert-base_copy_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 26 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: Frozen... | [
-0.018565448001027107,
-0.01716761477291584,
-0.00764684472233057,
0.028899122029542923,
0.051917433738708496,
-0.016849029809236526,
-0.01183371338993311,
-0.007737497799098492,
-0.058341991156339645,
0.05459960177540779,
-0.0022934486623853445,
-0.008824898861348629,
0.02519972249865532,
... |
AnonymousSub/unsup-consert-papers-bert | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 9 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fedcsis_translated-intent_baseline-xlm_r-pl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then r... | [
-0.02005271054804325,
-0.01849042810499668,
0.002287193899974227,
0.03860648721456528,
0.037297628819942474,
0.03424558788537979,
-0.005519021302461624,
-0.02562764100730419,
-0.02641228586435318,
0.06381677836179733,
0.012753148563206196,
-0.04550560191273689,
0.009091001935303211,
0.0290... |
AnonymousSubmission/pretrained-model-1 | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxi-v3-simple
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.44 +... | [
-0.02090798132121563,
-0.015160000883042812,
-0.007491148076951504,
0.023334242403507233,
0.046574365347623825,
-0.0033131535165011883,
-0.01823032647371292,
0.00857995729893446,
-0.036665692925453186,
0.053686171770095825,
0.014520424418151379,
-0.005451546516269445,
0.013736270368099213,
... |
AnthonyNelson/DialoGPT-small-ricksanchez | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fedcsis_translated-slot_baseline-xlm_r-pl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and ... | [
-0.003107762662693858,
-0.004431023728102446,
-0.001972595229744911,
0.029550643637776375,
0.03730184957385063,
0.025762835517525673,
-0.017116952687501907,
-0.020857328549027443,
-0.04464460164308548,
0.06345539540052414,
0.011085662059485912,
-0.05277528613805771,
0.00840954203158617,
0.... |
Anthos23/my-awesome-model | [
"pytorch",
"tf",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 30 | 2023-03-27T22:24:17Z | # Vocabulary Trimmed [lmqg/mbart-large-cc25-jaquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-jaquad-qa): `vocabtrimmer/mbart-large-cc25-jaquad-qa-trimmed-ja-60000`
This model is a trimmed version of [lmqg/mbart-large-cc25-jaquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-jaquad-qa) by [`vocabtrimmer`](htt... | [
-0.018382403999567032,
-0.020495323464274406,
-0.025452665984630585,
0.041837867349386215,
0.022946935147047043,
-0.006030563730746508,
-0.002672808710485697,
0.004456433933228254,
-0.035427242517471313,
0.038424596190452576,
0.0004414504219312221,
-0.03212990239262581,
0.02104831486940384,
... |
Anthos23/test_trainer | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
... | [
-0.005944718141108751,
0.0024283388629555702,
-0.02684902772307396,
0.041945911943912506,
0.04637845978140831,
0.01531525980681181,
-0.03309699147939682,
-0.023822154849767685,
-0.027804331853985786,
0.053409017622470856,
0.007259295787662268,
-0.01410193182528019,
0.019194357097148895,
0.... |
Anubhav23/model_name | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2023-03-27T22:37:01Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
... | [
-0.03942708671092987,
-0.01650802232325077,
-0.013764017261564732,
0.03662901371717453,
0.048682425171136856,
-0.0055886381305754185,
-0.012161352671682835,
-0.02524908445775509,
-0.03089103288948536,
0.05534927174448967,
0.023914629593491554,
-0.03237461671233177,
0.016874544322490692,
0.... |
Anupam/QuestionClassifier | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
datasets:
- mc4
language:
- pt
metrics:
- perplexity
library_name: transformers
---
This model is a Portuguese fine-tuned version of the [facebook/opt-125m](https://huggingface.co/facebook/opt-125m). It has undergone additional causal language modeling pre-training with a context size of 512, using an extra 300 mi... | [
-0.033161461353302,
-0.016435246914625168,
0.0019494890002533793,
0.04627453535795212,
0.025187253952026367,
0.04236917570233345,
-0.008052205666899681,
0.01003896165639162,
-0.022543951869010925,
0.07181046903133392,
0.02977604605257511,
-0.00747290626168251,
-0.015432906337082386,
0.0395... |
gaurishhs/API | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: 20230328-001-baseline-xlmr-clickbait-spoiling
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this commen... | [
-0.04917246475815773,
-0.004544597584754229,
0.009828022681176662,
0.03987402841448784,
0.045290980488061905,
0.016246432438492775,
-0.013772091828286648,
-0.02194404788315296,
-0.025659842416644096,
0.04294558987021446,
0.03894570469856262,
-0.03825599327683449,
0.004552694037556648,
0.02... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.