Fixed Clippy warnings (3)

examples/natural_language_inference_deberta.rs

@@ -38,7 +38,7 @@ fn main() -> anyhow::Result<()> {
         false,
     )?;
     let config = DebertaConfig::from_file(config_path);
-    let model = DebertaForSequenceClassification::new(&vs.root(), &config);
+    let model = DebertaForSequenceClassification::new(vs.root(), &config);
     vs.load(weights_path)?;
 
     //    Define input

tests/albert.rs

@@ -35,7 +35,7 @@ fn albert_masked_lm() -> anyhow::Result<()> {
     let tokenizer: AlbertTokenizer =
         AlbertTokenizer::from_file(vocab_path.to_str().unwrap(), true, false)?;
     let config = AlbertConfig::from_file(config_path);
-    let albert_model = AlbertForMaskedLM::new(&vs.root(), &config);
+    let albert_model = AlbertForMaskedLM::new(vs.root(), &config);
     vs.load(weights_path)?;
 
     //    Define input
@@ -109,7 +109,7 @@ fn albert_for_sequence_classification() -> anyhow::Result<()> {
     config.id2label = Some(dummy_label_mapping);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let albert_model = AlbertForSequenceClassification::new(&vs.root(), &config);
+    let albert_model = AlbertForSequenceClassification::new(vs.root(), &config);
 
     //    Define input
     let input = [
@@ -170,7 +170,7 @@ fn albert_for_multiple_choice() -> anyhow::Result<()> {
     let mut config = AlbertConfig::from_file(config_path);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let albert_model = AlbertForMultipleChoice::new(&vs.root(), &config);
+    let albert_model = AlbertForMultipleChoice::new(vs.root(), &config);
 
     //    Define input
     let input = [
@@ -242,7 +242,7 @@ fn albert_for_token_classification() -> anyhow::Result<()> {
     config.id2label = Some(dummy_label_mapping);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let albert_model = AlbertForTokenClassification::new(&vs.root(), &config);
+    let albert_model = AlbertForTokenClassification::new(vs.root(), &config);
 
     //    Define input
     let input = [
@@ -303,7 +303,7 @@ fn albert_for_question_answering() -> anyhow::Result<()> {
     let mut config = AlbertConfig::from_file(config_path);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let albert_model = AlbertForQuestionAnswering::new(&vs.root(), &config);
+    let albert_model = AlbertForQuestionAnswering::new(vs.root(), &config);
 
     //    Define input
     let input = [

tests/bert.rs

@@ -35,7 +35,7 @@ fn bert_masked_lm() -> anyhow::Result<()> {
     let tokenizer: BertTokenizer =
         BertTokenizer::from_file(vocab_path.to_str().unwrap(), true, true)?;
     let config = BertConfig::from_file(config_path);
-    let bert_model = BertForMaskedLM::new(&vs.root(), &config);
+    let bert_model = BertForMaskedLM::new(vs.root(), &config);
     vs.load(weights_path)?;
 
     //    Define input
@@ -162,7 +162,7 @@ fn bert_for_sequence_classification() -> anyhow::Result<()> {
     config.id2label = Some(dummy_label_mapping);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let bert_model = BertForSequenceClassification::new(&vs.root(), &config);
+    let bert_model = BertForSequenceClassification::new(vs.root(), &config);
 
     //    Define input
     let input = [
@@ -219,7 +219,7 @@ fn bert_for_multiple_choice() -> anyhow::Result<()> {
     let mut config = BertConfig::from_file(config_path);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let bert_model = BertForMultipleChoice::new(&vs.root(), &config);
+    let bert_model = BertForMultipleChoice::new(vs.root(), &config);
 
     //    Define input
     let input = [
@@ -283,7 +283,7 @@ fn bert_for_token_classification() -> anyhow::Result<()> {
     config.id2label = Some(dummy_label_mapping);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let bert_model = BertForTokenClassification::new(&vs.root(), &config);
+    let bert_model = BertForTokenClassification::new(vs.root(), &config);
 
     //    Define input
     let input = [
@@ -340,7 +340,7 @@ fn bert_for_question_answering() -> anyhow::Result<()> {
     let mut config = BertConfig::from_file(config_path);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let bert_model = BertForQuestionAnswering::new(&vs.root(), &config);
+    let bert_model = BertForQuestionAnswering::new(vs.root(), &config);
 
     //    Define input
     let input = [

tests/deberta.rs

@@ -41,7 +41,7 @@ fn deberta_natural_language_inference() -> anyhow::Result<()> {
         false,
     )?;
     let config = DebertaConfig::from_file(config_path);
-    let model = DebertaForSequenceClassification::new(&vs.root(), &config);
+    let model = DebertaForSequenceClassification::new(vs.root(), &config);
     vs.load(weights_path)?;
 
     //    Define input
@@ -96,7 +96,7 @@ fn deberta_masked_lm() -> anyhow::Result<()> {
     let mut config = DebertaConfig::from_file(config_path);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let deberta_model = DebertaForMaskedLM::new(&vs.root(), &config);
+    let deberta_model = DebertaForMaskedLM::new(vs.root(), &config);
 
     //    Generate random input
     let input_tensor = Tensor::randint(42, &[32, 128], (Kind::Int64, device));
@@ -170,7 +170,7 @@ fn deberta_for_token_classification() -> anyhow::Result<()> {
     dummy_label_mapping.insert(2, String::from("PER"));
     dummy_label_mapping.insert(3, String::from("ORG"));
     config.id2label = Some(dummy_label_mapping);
-    let model = DebertaForTokenClassification::new(&vs.root(), &config);
+    let model = DebertaForTokenClassification::new(vs.root(), &config);
 
     //    Define input
     let inputs = ["Where's Paris?", "In Kentucky, United States"];
@@ -225,7 +225,7 @@ fn deberta_for_question_answering() -> anyhow::Result<()> {
         false,
     )?;
     let config = DebertaConfig::from_file(config_path);
-    let model = DebertaForQuestionAnswering::new(&vs.root(), &config);
+    let model = DebertaForQuestionAnswering::new(vs.root(), &config);
 
     //    Define input
     let inputs = ["Where's Paris?", "Paris is in In Kentucky, United States"];

tests/deberta_v2.rs

@@ -22,7 +22,7 @@ fn deberta_v2_masked_lm() -> anyhow::Result<()> {
     let mut config = DebertaV2Config::from_file(config_path);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let deberta_model = DebertaV2ForMaskedLM::new(&vs.root(), &config);
+    let deberta_model = DebertaV2ForMaskedLM::new(vs.root(), &config);
 
     //    Generate random input
     let input_tensor = Tensor::randint(42, &[32, 128], (Kind::Int64, device));
@@ -88,7 +88,7 @@ fn deberta_v2_for_sequence_classification() -> anyhow::Result<()> {
     dummy_label_mapping.insert(1, String::from("Neutral"));
     dummy_label_mapping.insert(2, String::from("Negative"));
     config.id2label = Some(dummy_label_mapping);
-    let model = DebertaV2ForSequenceClassification::new(&vs.root(), &config);
+    let model = DebertaV2ForSequenceClassification::new(vs.root(), &config);
 
     //    Define input
     let inputs = ["Where's Paris?", "In Kentucky, United States"];
@@ -142,7 +142,7 @@ fn deberta_v2_for_token_classification() -> anyhow::Result<()> {
     dummy_label_mapping.insert(2, String::from("PER"));
     dummy_label_mapping.insert(3, String::from("ORG"));
     config.id2label = Some(dummy_label_mapping);
-    let model = DebertaV2ForTokenClassification::new(&vs.root(), &config);
+    let model = DebertaV2ForTokenClassification::new(vs.root(), &config);
 
     //    Define input
     let inputs = ["Where's Paris?", "In Kentucky, United States"];
@@ -190,7 +190,7 @@ fn deberta_v2_for_question_answering() -> anyhow::Result<()> {
     let tokenizer =
         DeBERTaV2Tokenizer::from_file(vocab_path.to_str().unwrap(), false, false, false)?;
     let config = DebertaV2Config::from_file(config_path);
-    let model = DebertaV2ForQuestionAnswering::new(&vs.root(), &config);
+    let model = DebertaV2ForQuestionAnswering::new(vs.root(), &config);
 
     //    Define input
     let inputs = ["Where's Paris?", "Paris is in In Kentucky, United States"];

tests/distilbert.rs

@@ -61,7 +61,7 @@ fn distilbert_masked_lm() -> anyhow::Result<()> {
     let tokenizer: BertTokenizer =
         BertTokenizer::from_file(vocab_path.to_str().unwrap(), true, true)?;
     let config = DistilBertConfig::from_file(config_path);
-    let distil_bert_model = DistilBertModelMaskedLM::new(&vs.root(), &config);
+    let distil_bert_model = DistilBertModelMaskedLM::new(vs.root(), &config);
     vs.load(weights_path)?;
 
     //    Define input
@@ -140,7 +140,7 @@ fn distilbert_for_question_answering() -> anyhow::Result<()> {
     let mut config = DistilBertConfig::from_file(config_path);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let distil_bert_model = DistilBertForQuestionAnswering::new(&vs.root(), &config);
+    let distil_bert_model = DistilBertForQuestionAnswering::new(vs.root(), &config);
 
     //    Define input
     let input = [
@@ -211,7 +211,7 @@ fn distilbert_for_token_classification() -> anyhow::Result<()> {
     dummy_label_mapping.insert(2, String::from("PER"));
     dummy_label_mapping.insert(3, String::from("ORG"));
     config.id2label = Some(dummy_label_mapping);
-    let distil_bert_model = DistilBertForTokenClassification::new(&vs.root(), &config);
+    let distil_bert_model = DistilBertForTokenClassification::new(vs.root(), &config);
 
     //    Define input
     let input = [

tests/distilgpt2.rs

@@ -37,7 +37,7 @@ fn distilgpt2_lm_model() -> anyhow::Result<()> {
         false,
     )?;
     let config = Gpt2Config::from_file(config_path);
-    let gpt2_model = GPT2LMHeadModel::new(&vs.root(), &config);
+    let gpt2_model = GPT2LMHeadModel::new(vs.root(), &config);
     vs.load(weights_path)?;
 
     //    Define input

tests/electra.rs

@@ -32,7 +32,7 @@ fn electra_masked_lm() -> anyhow::Result<()> {
     let mut config = ElectraConfig::from_file(config_path);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let electra_model = ElectraForMaskedLM::new(&vs.root(), &config);
+    let electra_model = ElectraForMaskedLM::new(vs.root(), &config);
     vs.load(weights_path)?;
 
     //    Define input
@@ -114,7 +114,7 @@ fn electra_discriminator() -> anyhow::Result<()> {
     let tokenizer: BertTokenizer =
         BertTokenizer::from_file(vocab_path.to_str().unwrap(), true, true)?;
     let config = ElectraConfig::from_file(config_path);
-    let electra_model = ElectraDiscriminator::new(&vs.root(), &config);
+    let electra_model = ElectraDiscriminator::new(vs.root(), &config);
     vs.load(weights_path)?;
 
     //    Define input

tests/fnet.rs

@@ -30,7 +30,7 @@ fn fnet_masked_lm() -> anyhow::Result<()> {
     let tokenizer: FNetTokenizer =
         FNetTokenizer::from_file(vocab_path.to_str().unwrap(), false, false)?;
     let config = FNetConfig::from_file(config_path);
-    let fnet_model = FNetForMaskedLM::new(&vs.root(), &config);
+    let fnet_model = FNetForMaskedLM::new(vs.root(), &config);
     vs.load(weights_path)?;
 
     //    Define input
@@ -138,7 +138,7 @@ fn fnet_for_multiple_choice() -> anyhow::Result<()> {
     let mut config = FNetConfig::from_file(config_path);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let fnet_model = FNetForMultipleChoice::new(&vs.root(), &config);
+    let fnet_model = FNetForMultipleChoice::new(vs.root(), &config);
 
     //    Define input
     let input = [
@@ -201,7 +201,7 @@ fn fnet_for_token_classification() -> anyhow::Result<()> {
     dummy_label_mapping.insert(3, String::from("ORG"));
     config.id2label = Some(dummy_label_mapping);
     config.output_hidden_states = Some(true);
-    let fnet_model = FNetForTokenClassification::new(&vs.root(), &config);
+    let fnet_model = FNetForTokenClassification::new(vs.root(), &config);
 
     //    Define input
     let input = [
@@ -256,7 +256,7 @@ fn fnet_for_question_answering() -> anyhow::Result<()> {
         FNetTokenizer::from_file(vocab_path.to_str().unwrap(), false, false)?;
     let mut config = FNetConfig::from_file(config_path);
     config.output_hidden_states = Some(true);
-    let fnet_model = FNetForQuestionAnswering::new(&vs.root(), &config);
+    let fnet_model = FNetForQuestionAnswering::new(vs.root(), &config);
 
     //    Define input
     let input = [

tests/gpt2.rs

@@ -35,7 +35,7 @@ fn gpt2_lm_model() -> anyhow::Result<()> {
         false,
     )?;
     let config = Gpt2Config::from_file(config_path);
-    let gpt2_model = GPT2LMHeadModel::new(&vs.root(), &config);
+    let gpt2_model = GPT2LMHeadModel::new(vs.root(), &config);
     vs.load(weights_path)?;
 
     //    Define input

tests/gpt_neo.rs

@@ -40,7 +40,7 @@ fn gpt_neo_lm() -> anyhow::Result<()> {
     let mut config = GptNeoConfig::from_file(config_path);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let gpt_neo_model = GptNeoForCausalLM::new(&vs.root(), &config)?;
+    let gpt_neo_model = GptNeoForCausalLM::new(vs.root(), &config)?;
     vs.load(weights_path)?;
 
     //    Define input

tests/longformer.rs

@@ -197,7 +197,7 @@ fn longformer_for_sequence_classification() -> anyhow::Result<()> {
     dummy_label_mapping.insert(1, String::from("Negative"));
     dummy_label_mapping.insert(3, String::from("Neutral"));
     config.id2label = Some(dummy_label_mapping);
-    let model = LongformerForSequenceClassification::new(&vs.root(), &config);
+    let model = LongformerForSequenceClassification::new(vs.root(), &config);
 
     //    Define input
     let input = ["Very positive sentence", "Second sentence input"];
@@ -258,7 +258,7 @@ fn longformer_for_multiple_choice() -> anyhow::Result<()> {
         false,
     )?;
     let config = LongformerConfig::from_file(config_path);
-    let model = LongformerForMultipleChoice::new(&vs.root(), &config);
+    let model = LongformerForMultipleChoice::new(vs.root(), &config);
 
     //    Define input
     let prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.";
@@ -337,7 +337,7 @@ fn longformer_for_token_classification() -> anyhow::Result<()> {
     dummy_label_mapping.insert(2, String::from("PER"));
     dummy_label_mapping.insert(3, String::from("ORG"));
     config.id2label = Some(dummy_label_mapping);
-    let model = LongformerForTokenClassification::new(&vs.root(), &config);
+    let model = LongformerForTokenClassification::new(vs.root(), &config);
 
     //    Define input
     let inputs = ["Where's Paris?", "In Kentucky, United States"];

tests/mobilebert.rs

@@ -35,7 +35,7 @@ fn mobilebert_masked_model() -> anyhow::Result<()> {
     let mut config = MobileBertConfig::from_file(config_path);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let mobilebert_model = MobileBertForMaskedLM::new(&vs.root(), &config);
+    let mobilebert_model = MobileBertForMaskedLM::new(vs.root(), &config);
     vs.load(weights_path)?;
 
     //    Define input
@@ -130,7 +130,7 @@ fn mobilebert_for_sequence_classification() -> anyhow::Result<()> {
     dummy_label_mapping.insert(1, String::from("Negative"));
     dummy_label_mapping.insert(3, String::from("Neutral"));
     config.id2label = Some(dummy_label_mapping);
-    let model = MobileBertForSequenceClassification::new(&vs.root(), &config);
+    let model = MobileBertForSequenceClassification::new(vs.root(), &config);
 
     //    Define input
     let input = ["Very positive sentence", "Second sentence input"];
@@ -176,7 +176,7 @@ fn mobilebert_for_multiple_choice() -> anyhow::Result<()> {
     let vs = nn::VarStore::new(device);
     let tokenizer = BertTokenizer::from_file(vocab_path.to_str().unwrap(), true, true)?;
     let config = MobileBertConfig::from_file(config_path);
-    let model = MobileBertForMultipleChoice::new(&vs.root(), &config);
+    let model = MobileBertForMultipleChoice::new(vs.root(), &config);
 
     //    Define input
     let prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.";
@@ -240,7 +240,7 @@ fn mobilebert_for_token_classification() -> anyhow::Result<()> {
     dummy_label_mapping.insert(2, String::from("PER"));
     dummy_label_mapping.insert(3, String::from("ORG"));
     config.id2label = Some(dummy_label_mapping);
-    let model = MobileBertForTokenClassification::new(&vs.root(), &config);
+    let model = MobileBertForTokenClassification::new(vs.root(), &config);
 
     //    Define input
     let inputs = ["Where's Paris?", "In Kentucky, United States"];
@@ -287,7 +287,7 @@ fn mobilebert_for_question_answering() -> anyhow::Result<()> {
     let vs = nn::VarStore::new(device);
     let tokenizer = BertTokenizer::from_file(vocab_path.to_str().unwrap(), true, true)?;
     let config = MobileBertConfig::from_file(config_path);
-    let model = MobileBertForQuestionAnswering::new(&vs.root(), &config);
+    let model = MobileBertForQuestionAnswering::new(vs.root(), &config);
 
     //    Define input
     let inputs = ["Where's Paris?", "Paris is in In Kentucky, United States"];

tests/openai_gpt.rs

@@ -39,7 +39,7 @@ fn openai_gpt_lm_model() -> anyhow::Result<()> {
         true,
     )?;
     let config = OpenAiGptConfig::from_file(config_path);
-    let openai_gpt = OpenAIGPTLMHeadModel::new(&vs.root(), &config);
+    let openai_gpt = OpenAIGPTLMHeadModel::new(vs.root(), &config);
     vs.load(weights_path)?;
 
     //    Define input

tests/reformer.rs

@@ -98,7 +98,7 @@ fn reformer_for_sequence_classification() -> anyhow::Result<()> {
     config.id2label = Some(dummy_label_mapping);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let reformer_model = ReformerForSequenceClassification::new(&vs.root(), &config)?;
+    let reformer_model = ReformerForSequenceClassification::new(vs.root(), &config)?;
 
     //    Define input
     let input = [
@@ -159,7 +159,7 @@ fn reformer_for_question_answering() -> anyhow::Result<()> {
     let mut config = ReformerConfig::from_file(config_path);
     config.output_attentions = Some(true);
     config.output_hidden_states = Some(true);
-    let reformer_model = ReformerForQuestionAnswering::new(&vs.root(), &config)?;
+    let reformer_model = ReformerForQuestionAnswering::new(vs.root(), &config)?;
 
     //    Define input
     let input = [