End of training

ed9f01b verified 16 days ago

71.6 kB

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:277492
  - loss:CachedMultipleNegativesRankingLoss
base_model: benjamintli/modernbert-code-v3-hard-negatives
widget:
  - source_sentence: >-
      // Uint is a helper routine that allocates a new uint value to store v and

      // returns a pointer to it.  This is useful when assigning optional
      parameters.
    sentences:
      - "func (c *Animation) GetCurrentTimeWithParams(v *AnimationGetCurrentTimeParams) (float64, error) {\n\tresp, err := gcdmessage.SendCustomReturn(c.target, c.target.GetSendCh(), &gcdmessage.ParamRequest{Id: c.target.GetId(), Method: \"Animation.getCurrentTime\", Params: v})\n\tif err != nil {\n\t\treturn 0, err\n\t}\n\n\tvar chromeData struct {\n\t\tResult struct {\n\t\t\tCurrentTime float64\n\t\t}\n\t}\n\n\tif resp == nil {\n\t\treturn 0, &gcdmessage.ChromeEmptyResponseErr{}\n\t}\n\n\t// test if error first\n\tcerr := &gcdmessage.ChromeErrorResponse{}\n\tjson.Unmarshal(resp.Data, cerr)\n\tif cerr != nil && cerr.Error != nil {\n\t\treturn 0, &gcdmessage.ChromeRequestErr{Resp: cerr}\n\t}\n\n\tif err := json.Unmarshal(resp.Data, &chromeData); err != nil {\n\t\treturn 0, err\n\t}\n\n\treturn chromeData.Result.CurrentTime, nil\n}"
      - "func Uint(v uint) *uint {\n\tp := new(uint)\n\t*p = v\n\treturn p\n}"
      - |-
        def after_init_app(self, app: FlaskUnchained):
                """
                Configure the JSON encoder for Flask to be able to serialize Enums,
                LocalProxy objects, and SQLAlchemy models.
                """
                self.set_json_encoder(app)
                app.before_first_request(self.register_model_resources)
  - source_sentence: |-
      Returns a template for the parent of this template.

      @throws ValidationException if the template has no parent.
    sentences:
      - "func BodyContainsOr(values ...string) ResponseCondition {\n\treturn func(res *http.Response) error {\n\t\tbody, err := ioutil.ReadAll(res.Body)\n\t\tif err != nil {\n\t\t\treturn fmt.Errorf(\"failed to read response body: %s\", err)\n\t\t}\n\n\t\tfor _, value := range values {\n\t\t\tif strings.Contains(string(body), value) {\n\t\t\t\treturn nil\n\t\t\t}\n\t\t}\n\t\treturn fmt.Errorf(\"could not find '%v' in body '%s'\", values, string(body))\n\t}\n}"
      - |-
        protected function after_update($result) {
                global $DB;

                if (!$result) {
                    $this->beforeupdate = null;
                    return;
                }

                // The parent ID has changed, we need to fix all the paths of the children.
                if ($this->beforeupdate->get('parentid') != $this->get('parentid')) {
                    $beforepath = $this->beforeupdate->get('path') . $this->get('id') . '/';

                    $like = $DB->sql_like('path', '?');
                    $likesearch = $DB->sql_like_escape($beforepath) . '%';

                    $table = '{' . self::TABLE . '}';
                    $sql = "UPDATE $table SET path = REPLACE(path, ?, ?) WHERE " . $like;
                    $DB->execute($sql, array(
                        $beforepath,
                        $this->get('path') . $this->get('id') . '/',
                        $likesearch
                    ));

                    // Resolving sortorder holes left after changing parent.
                    $table = '{' . self::TABLE . '}';
                    $sql = "UPDATE $table SET sortorder = sortorder -1 "
                            . " WHERE  competencyframeworkid = ? AND parentid = ? AND sortorder > ?";
                    $DB->execute($sql, array($this->get('competencyframeworkid'),
                                                $this->beforeupdate->get('parentid'),
                                                $this->beforeupdate->get('sortorder')
                                            ));
                }

                $this->beforeupdate = null;
            }
      - |-
        public PathTemplate parentTemplate() {
            int i = segments.size();
            Segment seg = segments.get(--i);
            if (seg.kind() == SegmentKind.END_BINDING) {
              while (i > 0 && segments.get(--i).kind() != SegmentKind.BINDING) {}
            }
            if (i == 0) {
              throw new ValidationException("template does not have a parent");
            }
            return new PathTemplate(segments.subList(0, i), urlEncoding);
          }
  - source_sentence: |-
      Build a potentially nested fieldgroup

      @param mixed $valueOrGroup Value of item, or title of group
      @param string|array $titleOrOptions Title of item, or options in grouip
      @return ArrayData Data for this item
    sentences:
      - |-
        protected function getFieldOption($valueOrGroup, $titleOrOptions)
            {
                // Return flat option
                if (!is_array($titleOrOptions)) {
                    return parent::getFieldOption($valueOrGroup, $titleOrOptions);
                }

                // Build children from options list
                $options = new ArrayList();
                foreach ($titleOrOptions as $childValue => $childTitle) {
                    $options->push($this->getFieldOption($childValue, $childTitle));
                }

                return new ArrayData(array(
                    'Title' => $valueOrGroup,
                    'Options' => $options
                ));
            }
      - |-
        public static function minify($content, array $options = [])
            {
                $min = preg_replace(['/[\n\r]/', '/\>[^\S ]+/s', '/[^\S ]+\</s', '/(\s)+/s', ], ['', '>', '<', '\\1'], trim($content));
                $min = str_replace(['> <'], ['><'], $min);
                
                if (ArrayHelper::getValue($options, 'comments', false)) {
                    $min = preg_replace('/<!--(.*)-->/Uis', '', $min);
                }
                
                return $min;
            }
      - |-
        private function loadXInclude(XInclude $xinclude, $filePath){
                //load DOMDocument
                $xml = new DOMDocument();
                $loadSuccess = $xml->load($filePath);
                $node = $xml->documentElement;
                if($loadSuccess && !is_null($node)){
                    //parse the href content
                    $parser = new ParserFactory($xml);
                    $parser->loadContainerStatic($node, $xinclude->getBody());
                }else{
                    throw new XIncludeException('Cannot load the XInclude DOM XML', $xinclude);
                }
            }
  - source_sentence: |-
      Check for new unread messages and send them to the custom api

          @param client_id: ID of client user
    sentences:
      - |-
        public function getLatMap()
            {
                if (null === $this->latMap) {
                    $this->latMap = $this->getTransliterationMap(Settings::ALPHABET_LAT);
                }

                return $this->latMap;
            }
      - |-
        def check_new_messages(client_id):
            """Check for new unread messages and send them to the custom api

            @param client_id: ID of client user
            """
            # Return if driver is not defined or if whatsapp is not logged in.
            # Stop the timer as well
            if client_id not in drivers or not drivers[client_id] or not drivers[client_id].is_logged_in():
                timers[client_id].stop()
                return

            # Acquire a lock on thread
            if not acquire_semaphore(client_id, True):
                return

            try:
                # Get all unread messages
                res = drivers[client_id].get_unread()
                # Mark all of them as seen
                for message_group in res:
                    message_group.chat.send_seen()
                # Release thread lock
                release_semaphore(client_id)
                # If we have new messages, do something with it
                if res:
                    print(res)
            except:
                pass
            finally:
                # Release lock anyway, safekeeping
                release_semaphore(client_id)
      - |-
        def get_uppermost_library_root_state(self):
                """Find state_copy of uppermost LibraryState

                Method checks if there is a parent library root state and assigns it to be the current library root state till
                there is no further parent library root state.
                """

                library_root_state = self.get_next_upper_library_root_state()
                parent_library_root_state = library_root_state
                # initial a library root state has to be found and if there is no further parent root state
                # parent_library_root_state and library_root_state are no more identical
                while parent_library_root_state and library_root_state is parent_library_root_state:
                    if library_root_state:
                        parent_library_root_state = library_root_state.parent.get_next_upper_library_root_state()

                    if parent_library_root_state:
                        library_root_state = parent_library_root_state

                return library_root_state
  - source_sentence: If MultiTenantMiddleware is used, filter queryset by request.site_id
    sentences:
      - |-
        def reduce_ticks(ax, which, maxticks=3):
            """Given a pyplot axis, resamples its `which`-axis ticks such that are at most
            `maxticks` left.

            Parameters
            ----------
            ax : axis
                The axis to adjust.
            which : {'x' | 'y'}
                Which axis to adjust.
            maxticks : {3, int}
                Maximum number of ticks to use.

            Returns
            -------
            array
                An array of the selected ticks.
            """
            ticks = getattr(ax, 'get_{}ticks'.format(which))()
            if len(ticks) > maxticks:
                # make sure the left/right value is not at the edge
                minax, maxax = getattr(ax, 'get_{}lim'.format(which))()
                dw = abs(maxax-minax)/10.
                start_idx, end_idx = 0, len(ticks)
                if ticks[0] < minax + dw:
                    start_idx += 1
                if ticks[-1] > maxax - dw:
                    end_idx -= 1
                # get reduction factor
                fac = int(len(ticks) / maxticks)
                ticks = ticks[start_idx:end_idx:fac]
            return ticks
      - |-
        function (isPublic, name, data, ttl, published_at, coreid) {
                var rawFn = function (msg) {
                    try {
                        msg.setMaxAge(parseInt((ttl && (ttl >= 0)) ? ttl : 60));
                        if (published_at) {
                            msg.setTimestamp(moment(published_at).toDate());
                        }
                    }
                    catch (ex) {
                        logger.error("onCoreHeard - " + ex);
                    }
                    return msg;
                };

                var msgName = (isPublic) ? "PublicEvent" : "PrivateEvent";
                var userID = (this.userID || "").toLowerCase() + "/";
                name = (name) ? name.toString() : name;
                if (name && name.indexOf && (name.indexOf(userID) == 0)) {
                    name = name.substring(userID.length);
                }

                data = (data) ? data.toString() : data;
                this.sendNONTypeMessage(msgName, { event_name: name, _raw: rawFn }, data);
            }
      - |-
        def get_queryset(self):
                '''
                If MultiTenantMiddleware is used, filter queryset by request.site_id
                '''
                queryset = super(PageList, self).get_queryset()
                if hasattr(self.request, 'site_id'):
                    queryset = queryset.filter(site_id=self.request.site_id)
                return queryset
datasets:
  - benjamintli/code-retrieval-hard-negatives-llm-verified-merged
  - benjamintli/code-retrieval-combined-v2
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: SentenceTransformer based on benjamintli/modernbert-code-v3-hard-negatives
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: eval
          type: eval
        metrics:
          - type: cosine_accuracy@1
            value: 0.8943333333333333
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.943
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.963
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.976
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8943333333333333
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.31433333333333335
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1926
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0976
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8943333333333333
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.943
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.963
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.976
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9359015737200269
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9229293650793654
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9239732035430454
            name: Cosine Map@100

SentenceTransformer based on benjamintli/modernbert-code-v3-hard-negatives

This is a sentence-transformers model finetuned from benjamintli/modernbert-code-v3-hard-negatives on the code-retrieval-hard-negatives-llm-verified-merged dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: benjamintli/modernbert-code-v3-hard-negatives
Maximum Sequence Length: 1024 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- code-retrieval-hard-negatives-llm-verified-merged

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False, 'architecture': 'OptimizedModule'})
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("modernbert-code-v4-hard-negatives")
# Run inference
queries = [
    "If MultiTenantMiddleware is used, filter queryset by request.site_id",
]
documents = [
    "def get_queryset(self):\n        '''\n        If MultiTenantMiddleware is used, filter queryset by request.site_id\n        '''\n        queryset = super(PageList, self).get_queryset()\n        if hasattr(self.request, 'site_id'):\n            queryset = queryset.filter(site_id=self.request.site_id)\n        return queryset",
    'def reduce_ticks(ax, which, maxticks=3):\n    """Given a pyplot axis, resamples its `which`-axis ticks such that are at most\n    `maxticks` left.\n\n    Parameters\n    ----------\n    ax : axis\n        The axis to adjust.\n    which : {\'x\' | \'y\'}\n        Which axis to adjust.\n    maxticks : {3, int}\n        Maximum number of ticks to use.\n\n    Returns\n    -------\n    array\n        An array of the selected ticks.\n    """\n    ticks = getattr(ax, \'get_{}ticks\'.format(which))()\n    if len(ticks) > maxticks:\n        # make sure the left/right value is not at the edge\n        minax, maxax = getattr(ax, \'get_{}lim\'.format(which))()\n        dw = abs(maxax-minax)/10.\n        start_idx, end_idx = 0, len(ticks)\n        if ticks[0] < minax + dw:\n            start_idx += 1\n        if ticks[-1] > maxax - dw:\n            end_idx -= 1\n        # get reduction factor\n        fac = int(len(ticks) / maxticks)\n        ticks = ticks[start_idx:end_idx:fac]\n    return ticks',
    'function (isPublic, name, data, ttl, published_at, coreid) {\n        var rawFn = function (msg) {\n            try {\n                msg.setMaxAge(parseInt((ttl && (ttl >= 0)) ? ttl : 60));\n                if (published_at) {\n                    msg.setTimestamp(moment(published_at).toDate());\n                }\n            }\n            catch (ex) {\n                logger.error("onCoreHeard - " + ex);\n            }\n            return msg;\n        };\n\n        var msgName = (isPublic) ? "PublicEvent" : "PrivateEvent";\n        var userID = (this.userID || "").toLowerCase() + "/";\n        name = (name) ? name.toString() : name;\n        if (name && name.indexOf && (name.indexOf(userID) == 0)) {\n            name = name.substring(userID.length);\n        }\n\n        data = (data) ? data.toString() : data;\n        this.sendNONTypeMessage(msgName, { event_name: name, _raw: rawFn }, data);\n    }',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.8836, -0.0275,  0.0176]])

Evaluation

Metrics

Information Retrieval

Dataset: eval
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8943
cosine_accuracy@3	0.943
cosine_accuracy@5	0.963
cosine_accuracy@10	0.976
cosine_precision@1	0.8943
cosine_precision@3	0.3143
cosine_precision@5	0.1926
cosine_precision@10	0.0976
cosine_recall@1	0.8943
cosine_recall@3	0.943
cosine_recall@5	0.963
cosine_recall@10	0.976
cosine_ndcg@10	0.9359
cosine_mrr@10	0.9229
cosine_map@100	0.924

Training Details

Training Dataset

code-retrieval-hard-negatives-llm-verified-merged

Dataset: code-retrieval-hard-negatives-llm-verified-merged at 459ec4b
Size: 277,492 training samples
Columns: query, positive, negative_0, negative_1, negative_2, negative_3, negative_4, and negative_5

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5
type	string	string	string	string	string	string	string	string
details	min: 6 tokens mean: 298.97 tokens max: 1024 tokens	min: 9 tokens mean: 191.06 tokens max: 1024 tokens	min: 16 tokens mean: 215.07 tokens max: 1024 tokens	min: 17 tokens mean: 214.94 tokens max: 1024 tokens	min: 16 tokens mean: 215.65 tokens max: 1024 tokens	min: 17 tokens mean: 215.93 tokens max: 1024 tokens	min: 15 tokens mean: 219.31 tokens max: 1024 tokens	min: 15 tokens mean: 219.5 tokens max: 1024 tokens

Samples:

query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5
A valid parentheses sequence is a non-empty string where each character is either '(' or ')', which satisfies the following constraint: You can find a way to repeat erasing adjacent pairs of parentheses '()' until it becomes empty. For example, '(())' and '()((()()))' are valid parentheses sequences, but ')()(' and '(()' are not. Mike has a valid parentheses sequence. He really likes everything about his sequence, except the fact that it is quite long. So Mike has recently decided that he will replace his parentheses sequence with a new one in the near future. But not every valid parentheses sequence will satisfy him. To help you understand his requirements we'll introduce the pseudocode of function F(S): FUNCTION F( S - a valid parentheses sequence ) BEGIN balance = 0 max_balance = 0 FOR index FROM 1 TO LENGTH(S) BEGIN if S[index] == '(' then balance = balance + 1 if S[index] == ')' then balance = balance - 1 max_balance = max( max_balance, balance ) END ...	`try: for i in range(int(input())): s=input() balance=0 max_balance=0 for i in s: if i=='(':balance+=1 else: balance-=1 max_balance=max(max_balance,balance) print('('max_balance,')'max_balance,sep="") except Exception as e: print(e)`	`t=int(input()) for tt in range(t): a,b,p=map(int,input().split()) s=input() n=len(s) cost = [0]*n cost[-1] = 0 typ = '' i=n-2 while i>=0: if s[i]==typ: cost[i] = cost[i+1] else: typ = s[i] cost[i] = cost[i+1] + (a if typ=='A' else b) i-=1 i=0 while cost[i] > p: i+=1 print(i+1)`	`test=int(input()) for i in range(test): s=input() b=len(s) list1=[] for j in range(len(s)): if s[j]=='.': list1.append(j) for i in list1: if b-i-1 in list1 : if i!=b-i-1 and ((s[i] and s[b-i-1]) != 'a' ): s=s[:i]+'a'+s[i+1:b-i-1]+'a'+s[b-i:] else: s=s[:i]+'a'+s[i+1:] else: s=s[:i]+s[b-i-1]+s[i+1:] if s==s[::-1]: print(s) else: print(-1)`	`from collections import Counter`
def solve(A,B):
a = Counter(A)
b = Counter(B)
ans = 0
for i in a:
if i in b:
ans += min(a[i],b[i])

return ans


t = int(input())

for _ in range(t):
A = input()
B = input()
print(solve(A,B))	`l=list(map(int,input())) t=-1 x=-1 y=-1 for i in range(len(l)): s=l[i] a=i+1 b=i+1 for j in range(i+1,len(l)): if l[i] s=s+l[j] b=j+1 else: break if s>t: t=s x=a y=b print(t,end=":") print(x,y,sep="-")`	`t=eval(input()) a=[] b=[] top=-1 for __ in range(0,t): x=input().split() if(x[0]!="-1" and x[0]!="0"): add=int(x[0]) if top!=-1 and add>a[top][0] : b[top]+=1 else: a.append((add,x[1])) b.append(0) top+=1 elif (x[0]=="-1"): #print("%s %s" %(b[top],a[top][1])) print((b[top]), end=' ') print(a[top][1]) foo=a.pop() bar=b.pop() top-=1`	`t=eval(input()) a=[] b=[] top=-1 for __ in range(0,t): x=input().split() if(x[0]!="-1" and x[0]!="0"): add=int(x[0]) if top!=-1 and add>a[top][0] : b[top]+=1 else: a.append((add,x[1])) b.append(0) top+=1 elif (x[0]=="-1"): #print("%s %s" %(b[top],a[top][1])) print((b[top]), end=' ') print(a[top][1]) foo=a.pop() bar=b.pop() top-=1`
Chef has a cubic die with 6 faces kept on an infinite plane. Each face has a distinct integer in the range [1,6] written on it, but the exact arrangement of the numbers on the faces of the die is unknown to Chef. Curiosity gets the better of Chef and he wants to find out o(1), o(2), ..., o(6), where o(i) is the number written opposite to the number i. Chef performs the following N-1 steps to learn the exact arrangement of the numbers on the die. In the i-th step, Chef pushes the die in some direction (there are 4 possible directions), and the die rolls 90o in this direction. The picture below demonstrates a die and the result that it produced after rolling in each of the 4 directions respectively. For this die, we have o(1)=4, o(2)=5, o(3)=6, o(4)=1, o(5)=2, o(6)=3. Chef records N numbers A1, A2, ..., AN, where Ai is the number written on the top of the die before the i-th step. However, the information on the direction in which he pushes the die each time are lost. Can you help h...	from itertools import permutations def solve(n,a): ans=[] for des in desire: check=1 for i in range(n-1): if (a[i]==a[i+1]): return [-1] if a[i+1]==des[a[i]-1]: check=0 break if check: ans=des break if ans: return ans return [-1] per=permutations([1,2,3,4,5,6]) desire=[] for p in per: check=1 for i in range(1,7): if p[i-1]==i: check=0 break if check: doublecheck=1 for i in range(6): if p[p[i]-1]!=i+1: doublecheck=0 break if doublecheck: desire.append(p) #print(desire) for _ in range(int(input())): n=int(input()) a=list(map(int,input().split( ))) print(*solve(n,a))	`def solve(): n = int(input()) lst = list(map(int,input().split())) if sum(lst) <= n // 2: print(n//2) print("0 " * (n // 2)) else: print(n//2 + (n // 2) % 2) print("1 " * (n//2 + (n // 2) % 2)) for i in range(int(input())): solve()`	`import sys input = lambda: sys.stdin.readline().rstrip() T = int(input()) for _ in range(T): N = int(input()) A = [int(a) for a in input().split()] if max(A) == min(A): print(1) print(([1] * N)) elif N % 2 == 0: print(2) print(([1, 2] * (N // 2))) else: for i in range(N): if A[i-1] == A[i]: print(2) print((([1, 2] * N)[:i][::-1] + ([1, 2] * N)[:N-i])) break else: print(3) print(([3] + [1, 2] * (N // 2)))`	`import numpy as np N=10**6+1 t=eval(input()) inp = () t1=ord('z') #bag=[[0 for _ in xrange(t1)] for _ in xrange(N+1)] bag=np.zeros((N+1,t1),dtype=np.int) #print bag while t: t-=1 inp=input().split() t2=ord(inp[3]) - ord('a') t3=int(inp[1]) t4=int(inp[2]) + 1 if inp[0]=="1": #print "enter" bag[t3][t2]+=int(inp[2]) if inp[0]=="2": sum=0 for i in range(t3,t4): sum+=bag[i][t2] print(sum) # # for j in range(ord('z')-ord('a')): # for i in range(N+1): # if bag[i][j]!=0: # print bag[i][j] ,i,j`	`# from math import log2 # N = 10000 # for i in range(1,N): # # print(i) # for m in range(i): # if( (m^(m+1))==i ): # print(i) # print(m,m+1,bin(m)[2:]) # print() # break # # else: # # print(-1) # # print() T = int(input()) ans = [] for _ in range(T): N = int(input()) # x = log2(N+1) if(N==1): ans.append(2) elif('0' not in bin(N)[2:]): ans.append(N//2) else: ans.append(-1) for i in ans: print(i)`	`# from math import log2 # N = 10000 # for i in range(1,N): # # print(i) # for m in range(i): # if( (m^(m+1))==i ): # print(i) # print(m,m+1,bin(m)[2:]) # print() # break # # else: # # print(-1) # # print() T = int(input()) ans = [] for _ in range(T): N = int(input()) # x = log2(N+1) if(N==1): ans.append(2) elif('0' not in bin(N)[2:]): ans.append(N//2) else: ans.append(-1) for i in ans: print(i)`	`# from math import log2 # N = 10000 # for i in range(1,N): # # print(i) # for m in range(i): # if( (m^(m+1))==i ): # print(i) # print(m,m+1,bin(m)[2:]) # print() # break # # else: # # print(-1) # # print() T = int(input()) ans = [] for _ in range(T): N = int(input()) # x = log2(N+1) if(N==1): ans.append(2) elif('0' not in bin(N)[2:]): ans.append(N//2) else: ans.append(-1) for i in ans: print(i)`
DevuLand is a very strange place. There are n villages in it. Some of the villages are occupied by dinosaurs while the remaining ones by villagers. You are given the information of DevuLand by an array D of size n. If D[i] is non-negative, it means that there are D[i] villagers in that village. Otherwise, it means that are -D[i] dinosaurs in that village. It is also guaranteed that total number of villagers in DevuLand is equal to total number of dinosaurs. Once dinosaurs got very hungry and started eating villagers. Frightened villagers gathered immediately and met their Sarpanch Deviji. Deviji, being a very daring and negotiable person, met to the head of dinosaurs. Soon both parties called a truce. It was decided that the villagers will provide laddus to the dinosaurs. So everyday, each villager will take exactly one laddu to one of the dinosaurs in such a way that no dinosaur remains hungry (note that this is possible because number of villagers is the same as the numbe...	`# cook your dish here for _ in range(int(input())): n = int(input()) a = list(map(int, input().split())) curr = 0 ans = 0 for x in a: curr += x ans += abs(curr) print(ans)`	`from collections import deque`
T=int(input())
def break_down(num):
count=0
while(len(num)!=1):
temp=0
for i in range(0,len(num)):
temp=temp+int(num[i])
num=str(temp)
count=count+1
return (int(num),count)
def digit_sum(num):
temp=0
for i in range(0,len(num)):
temp=temp+int(num[i])
num=temp
return (num)
while(T):
queue=deque()
count_n=0
count_d=0
T=T-1
N,d=[i for i in input().split()]
n,count_n=break_down(N)
D,count_D=break_down(d)
dic={}
if(D==1 or D==2 or D==4 or D==5 or D==7 or D==8):
mini=1
elif(D==3 or D==6):
mini=min(digit_sum(str(n+3)),digit_sum(str(n+6)),digit_sum(str(n+9)))
else:
mini=n
queue.append((int(N),0))
ele=int(N)
count=0
while(len(queue)!=0):
ele,count=queue.popleft()
if(ele==mini):
break
else:
if(len(str(ele))==1):
temp1=ele+int(d)
queue.append((temp1,count+1))...	# cook your dish here test_cases = int(input()) for i in range(test_cases): no_of_elements = int(input()) sequence = list(map(int, input().split())) d1 = sequence[1] - sequence[0] d2 = sequence[2] - sequence[1] d3 = (sequence[3] - sequence[0])/3 d4 = (sequence[3] - sequence[1])/2 d5 = (sequence[2] - sequence[0])/2 if (d2 == d4): d = d2 elif(d3 == d5): d = d3 elif(d1 == d3): d = d1 elif(d1 == d5): d = d1 if (d == d1): for i in range(no_of_elements): sequence[i] = int(sequence[0] + id) else: for i in range(no_of_elements): sequence[i] = int(sequence[-1] - ((no_of_elements - i - 1)d)) for i in sequence: print(i, end=" ") print('\n')	`from collections import Counter try: for _ in range(int(input())): n=int(input()) s=input() d1=dict(Counter(s)) u,d,r,l=0,0,0,0 if 'U' in d1: u=d1['U'] else: u=0 if 'D' in d1: d=d1['D'] else: d=0 if 'R' in d1: r=d1['R'] else: r=0 if 'L' in d1: l=d1['L'] else: l=0 x=0 y=0 if l==r: x=0 elif l>r: x=-(l-r) elif r>l: x=r-l if u==d: y=0 elif d>u: y=-(d-u) elif u>d: y=u-d # print(x,y) if x==0 and y==0: print(n) continue print(n-(abs(x)+abs(y))) except: pass`	from bisect import bisect_left, insort_left a = [] n = int(input()) for _ in range(n): #print(a) s, d = list(map(int, input().split())) if len(a) == 0: print(s, s+d - 1) a.append((s, s + d - 1)) continue p = bisect_left(a, (s, s + d - 1)) #print('p', p) ok = True if p > 0 and a[p-1][1] >= s: ok = False if p < len(a) and a[p][0] <= s + d - 1: ok = False if ok: insort_left(a, (s, s + d - 1)) print(s, s + d - 1) else: ok = False for i in range(len(a)): if i == 0: if a[0][0] > d: print(1,d) a = [(1, d)] + a ok = True break else: if a[i - 1][1] + d < a[i][0]: print(a[i - 1][1] + 1, a[i - 1][1] + d) insort_left(a, (a[i - 1][1] + 1, a[i - 1][1] + d)) ok = True break ...	`import fractions for t in range(int(input())): h,u,d = list(map(int,input().split())) g = fractions.gcd(u,d) if (h%g!=0): print(-1) else: m = 0 n = 0 while (True): n = (float(m)*u-h)/d if (n>0 and int(n) == n): break m+=1 print(int(m+n))`	`import fractions for t in range(int(input())): h,u,d = list(map(int,input().split())) g = fractions.gcd(u,d) if (h%g!=0): print(-1) else: m = 0 n = 0 while (True): n = (float(m)*u-h)/d if (n>0 and int(n) == n): break m+=1 print(int(m+n))`

Loss: CachedMultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "mini_batch_size": 128,
    "gather_across_devices": false,
    "directions": [
        "query_to_doc"
    ],
    "partition_mode": "joint",
    "hardness_mode": null,
    "hardness_strength": 0.0
}

Evaluation Dataset

code-retrieval-combined-v2

Dataset: code-retrieval-combined-v2 at 2b971a6
Size: 31,516 evaluation samples
Columns: query and positive
Approximate statistics based on the first 1000 samples:
query positive
type string string
details
min: 5 tokens
mean: 42.73 tokens
max: 834 tokens

min: 30 tokens
mean: 180.42 tokens
max: 1024 tokens

	query	positive
type	string	string
details	min: 5 tokens mean: 42.73 tokens max: 834 tokens	min: 30 tokens mean: 180.42 tokens max: 1024 tokens

Samples:

query	positive
`This gets the version of OpenALPR :return: Version information`	`def get_version(self): """ This gets the version of OpenALPR :return: Version information """ ptr = self._get_version_func(self.alpr_pointer) version_number = ctypes.cast(ptr, ctypes.c_char_p).value version_number = _convert_from_charp(version_number) self._free_json_mem_func(ctypes.c_void_p(ptr)) return version_number`
`Remove all unnecessary comments from a lexer or parser file`	`public String stripUnnecessaryComments(String javaContent, AntlrOptions options) { if (!options.isOptimizeCodeQuality()) { return javaContent; } javaContent = stripMachineDependentPaths(javaContent); if (options.isStripAllComments()) { javaContent = stripAllComments(javaContent); } return javaContent; }`
`Serialize reply to array or JSON. @param {Object} packet @param {String} packet.method "get", "search", "post", "put", "delete", "sub", "unsub". @param {String} packet.resource @param {String} packet.id @param {*} packet.body @param {Number} [packet.status] @param {Number\|String} [packet.date] @param {Object} [packet.headers] @param {Boolean} [json] true to generate JSON instead of array. @returns {Array\|String\|null}`	`function reply(packet, json) { return _create(packet, packet.status \|\| 500, (METHODS[packet.method] \|\| '') + packet.resource, json); }`

Loss: CachedMultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "mini_batch_size": 128,
    "gather_across_devices": false,
    "directions": [
        "query_to_doc"
    ],
    "partition_mode": "joint",
    "hardness_mode": null,
    "hardness_strength": 0.0
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 1024
per_device_eval_batch_size: 1024
num_train_epochs: 1
warmup_steps: 0.05
bf16: True
dataloader_num_workers: 4
load_best_model_at_end: True
push_to_hub: True
hub_model_id: modernbert-code-v4-hard-negatives
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 1024
per_device_eval_batch_size: 1024
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_ratio: None
warmup_steps: 0.05
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
enable_jit_checkpoint: False
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
use_cpu: False
seed: 42
data_seed: None
bf16: True
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: -1
ddp_backend: None
debug: []
dataloader_drop_last: False
dataloader_num_workers: 4
dataloader_prefetch_factor: None
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
push_to_hub: True
resume_from_checkpoint: None
hub_model_id: modernbert-code-v4-hard-negatives
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_for_metrics: []
eval_do_concat_batches: True
auto_find_batch_size: False
full_determinism: False
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
use_cache: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	eval_cosine_ndcg@10
0.0738	20	0.9880	-	-
0.1476	40	0.9529	0.3465	0.9286
0.2214	60	0.9726	-	-
0.2952	80	0.9299	0.3351	0.9296
0.3690	100	0.9130	-	-
0.4428	120	0.9187	0.3253	0.9325
0.5166	140	0.8940	-	-
0.5904	160	0.9037	0.3186	0.9354
0.6642	180	0.8951	-	-
0.738	200	0.8816	0.3121	0.9361
0.8118	220	0.8753	-	-
0.8856	240	0.8649	0.3106	0.9359
0.9594	260	0.8575	-	-

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.13
Sentence Transformers: 5.3.0
Transformers: 5.0.0
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0
Datasets: 4.0.0
Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}