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tokenizer = HerbertTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
model = RobertaModel.from_pretrained("allegro/herbert-klej-cased-v1")

encoded_input = tokenizer.encode("Kto ma lepsz\u0105 sztuk\u0119, ma lepszy rz\u0105d \u2013 to jasne.", return_tensors="pt")
outputs = model(encoded_input)

# HerBERT can also be loaded using AutoTokenizer and AutoModel:
import torch
from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
model = AutoModel.from_pretrained("allegro/herbert-klej-cased-v1")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> HerbertTokenizer, RobertaModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = HerbertTokenizer.from_pretrained(<span class="hljs-string">&quot;allegro/herbert-klej-cased-tokenizer-v1&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaModel.from_pretrained(<span class="hljs-string">&quot;allegro/herbert-klej-cased-v1&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>encoded_input = tokenizer.encode(<span class="hljs-string">&quot;Kto ma lepsz\u0105 sztuk\u0119, ma lepszy rz\u0105d \u2013 to jasne.&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(encoded_input)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># HerBERT can also be loaded using AutoTokenizer and AutoModel:</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel, AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;allegro/herbert-klej-cased-tokenizer-v1&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;allegro/herbert-klej-cased-v1&quot;</span>)`}}),te=new Ct({}),se=new M({props:{name:"class transformers.HerbertTokenizer",anchor:"transformers.HerbertTokenizer",parameters:[{name:"vocab_file",val:""},{name:"merges_file",val:""},{name:"tokenizer_file",val:" = None"},{name:"cls_token",val:" = '<s>'"},{name:"unk_token",val:" = '<unk>'"},{name:"pad_token",val:" = '<pad>'"},{name:"mask_token",val:" = '<mask>'"},{name:"sep_token",val:" = '</s>'"},{name:"bos_token",val:" = '<s>'"},{name:"do_lowercase_and_remove_accent",val:" = False"},{name:"additional_special_tokens",val:" = ['<special0>', '<special1>', '<special2>', '<special3>', '<special4>', '<special5>', '<special6>', '<special7>', '<special8>', '<special9>']"},{name:"lang2id",val:" = None"},{name:"id2lang",val:" = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/herbert/tokenization_herbert.py#L277"}}),oe=new M({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.HerbertTokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.HerbertTokenizer.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
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<p><code>List[int]</code></p>
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sentiment analysis task for the e-commerce domain, named Allegro Reviews (AR). To ensure a common evaluation scheme and
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sentiment analysis task for the e-commerce domain, named Allegro Reviews (AR). To ensure a common evaluation scheme and
promote models that generalize to different NLU tasks, the benchmark includes datasets from varying domains and
applications. Additionally, we release HerBERT, a Transformer-based model trained specifically for the Polish language,
which has the best average performance and obtains the best results for three out of nine tasks. Finally, we provide an
extensive evaluation, including several standard baselines and recently proposed, multilingual Transformer-based
models.`),Ar.forEach(s),xr.forEach(s),gt=c(t),Ee=n(t,"P",{});var Dr=o(Ee);Gt=i(Dr,"Examples of use:"),Dr.forEach(s),bt=c(t),$(Y.$$.fragment,t),vt=c(t),j=n(t,"P",{});var je=o(j);Qt=i(je,"This model was contributed by "),Z=n(je,"A",{href:!0,rel:!0});var Rr=o(Z);Yt=i(Rr,"rmroczkowski"),Rr.forEach(s),Zt=i(je,`. The original code can be found
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